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Промт для теста text-to-SQL 5 (archeology_4)

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  1. <НАЗВАНИЕ ТЕСТА>archeology_4</НАЗВАНИЕ ТЕСТА>
  2. <ЗАДАНИЕ ДЛЯ НЕЙРОСЕТИ>Нужно преобразовать запрос в свободной форме (тег «ИСХОДНЫЙ ТЕКСТОВЫЙ ЗАПРОС НА АНГЛИЙСКОМ») в SQL-запрос, воспользовавшись схемой базы данных (тег «СХЕМА БАЗЫ ДАННЫХ») и словарём знаний (тег «СЛОВАРЬ ЗНАНИЙ, СВЯЗАННЫЙ С БАЗОЙ ДАННЫХ»). При наличии комментариев и пояснений пишите их на русском языке.</ЗАДАНИЕ ДЛЯ НЕЙРОСЕТИ>
  3. <ИСХОДНЫЙ ТЕКСТОВЫЙ ЗАПРОС НА АНГЛИЙСКОМ>I’m evaluating the quality of our scan registrations. For each scan type, find the average Scan Resolution Index (SRI) rounded to 2 decimal places, and what confidence level that translates to.</ИСХОДНЫЙ ТЕКСТОВЫЙ ЗАПРОС НА АНГЛИЙСКОМ>
  4. <ПЕРЕВОД ИСХОДНОГО ТЕКСТОВОГО ЗАПРОСА НА РУССКИЙ (ДЛЯ ИНФОРМАЦИИ)>Я оцениваю качество регистрации сканов. Для каждого типа скана вычислите средний индекс разрешения (SRI) (округлён до 2 десятичных знаков) и соответствующий уровень доверия.</ПЕРЕВОД ИСХОДНОГО ТЕКСТОВОГО ЗАПРОСА НА РУССКИЙ (ДЛЯ ИНФОРМАЦИИ)>
  5. <СХЕМА БАЗЫ ДАННЫХ>
  6. CREATE TABLE "projects" (
  7. arcregistry character varying NOT NULL, /* 'Project ID'. Explanation: Primary key for Projects, a unique project identifier. Data type: VARCHAR(10). Example: 'PR7509'. */
  8. vesseltag character varying NULL, /* 'Project Name'. Explanation: Label or name for the project. Data type: VARCHAR(60). Example: 'Project Happy'. */
  9. fundflux text NULL, /* 'Funding Source'. Explanation: Source of funds for the project. Data type: TEXT. Example: 'Government'. */
  10. authpin character NULL, /* 'Permit Number'. Explanation: Authorization or permit ID. Data type: CHAR(6). Example: 'PMT4719'. */
  11. authhalt date NULL, /* 'Permit Expiry Date'. Explanation: Date on which the permit expires. Data type: DATE. Example: '2025-12-05'. */
  12. PRIMARY KEY (arcregistry)
  13. );
  14.  
  15. First 3 rows:
  16. arcregistry vesseltag fundflux authpin authhalt
  17. ------------- --------------- ---------- --------- ----------
  18. PR7509 Project Happy Government PMT4719 2025-12-05
  19. PR8078 Project Off Government PMT4944 2025-09-20
  20. PR9973 Project Central University PMT5400 2025-03-18
  21. ...
  22.  
  23.  
  24. CREATE TABLE "scanmesh" (
  25. facetregistry bigint NOT NULL DEFAULT nextval('scanmesh_facetregistry_seq'::regclass), /* 'Mesh Record ID'. Explanation: Primary key for mesh data. Data type: BIGSERIAL. Example: 3001. */
  26. zoneref character varying NOT NULL, /* 'Site Reference'. Explanation: Associates mesh data with a site code. Data type: VARCHAR(12). Example: 'SC9016'. */
  27. equipref character NOT NULL, /* 'Equipment Reference'. Explanation: Identifies which equipment was used to generate the mesh. Data type: CHAR(10). Example: 'SN20065'. */
  28. facetverts bigint NULL, /* 'Mesh Vertices'. Explanation: Count of mesh vertices. Data type: BIGINT. Example: 7234721. */
  29. facetfaces bigint NULL, /* 'Mesh Faces'. Explanation: Count of triangular or polygonal faces. Data type: BIGINT. Example: 5997318. */
  30. facetresmm numeric NULL, /* 'Mesh Resolution (mm)'. Explanation: Mesh vertex spacing in mm. Data type: NUMERIC(5,2). Example: 3.2. */
  31. texdist character varying NULL, /* 'Texture Resolution Setting'. Explanation: Preset texture resolution. Data type: VARCHAR(5). Possible categories: 2K, 1K, 4K. */
  32. texpix integer NULL, /* 'Texture Size (px)'. Explanation: Texture image dimension in pixels. Data type: INTEGER. Example: 2048. */
  33. uvmapqual character varying NULL, /* 'UV Mapping Quality'. Explanation: Quality of the UV mapping process. Data type: VARCHAR(10). Possible categories: Medium, High, Low. */
  34. geomdeltamm numeric NULL, /* 'Geometric Accuracy (mm)'. Explanation: Estimated geometric deviation in millimeters. Data type: NUMERIC(6,3). Example: 2.74. */
  35. PRIMARY KEY (facetregistry),
  36. FOREIGN KEY (equipref) REFERENCES equipment(equipregistry),
  37. FOREIGN KEY (zoneref) REFERENCES sites(zoneregistry)
  38. );
  39.  
  40. First 3 rows:
  41. facetregistry zoneref equipref facetverts facetfaces facetresmm texdist texpix uvmapqual geomdeltamm
  42. --------------- --------- ---------- ------------ ------------ ------------ --------- -------- ----------- -------------
  43. 1 SC9016 SN20065 7234721 5997318 3.2 2K 2048 Medium 2.74
  44. 2 SC9081 SN29799 2361491 7708278 9.79 2K 8192 High 3.79
  45. 3 SC4817 SN83019 542100 1973487 2.33 1K 4096 Low 0.48
  46. ...
  47.  
  48.  
  49. CREATE TABLE "scanprocessing" (
  50. flowregistry bigint NOT NULL DEFAULT nextval('scanprocessing_flowregistry_seq'::regclass), /* 'Processing Record ID'. Explanation: Primary key for processing workflow. Data type: BIGSERIAL. Example: 8001. */
  51. equipref character NOT NULL, /* 'Equipment Reference'. Explanation: Equipment ID used for processing. Data type: CHAR(10). Example: 'SN20065'. */
  52. zoneref character varying NOT NULL, /* 'Site Reference'. Explanation: Site code for which processing applies. Data type: VARCHAR(12). Example: 'SC9016'. */
  53. flowsoft character varying NULL, /* 'Processing Software'. Explanation: Name of the software used. Data type: VARCHAR(25). Possible categories: RealityCapture, CloudCompare, Agisoft. */
  54. flowhrs numeric NULL, /* 'Processing Time (hours)'. Explanation: Time spent on processing. Data type: NUMERIC(5,2). Example: 21.9. */
  55. proccpu smallint NULL, /* 'CPU Usage (%)'. Explanation: CPU usage as an integer percentage. Data type: SMALLINT. Example: 81. */
  56. memusagegb numeric NULL, /* 'Memory Usage (GB)'. Explanation: RAM usage during processing. Data type: NUMERIC(6,2). Example: 70.3. */
  57. procgpu smallint NULL, /* 'GPU Usage (%)'. Explanation: GPU usage as an integer percentage. Data type: SMALLINT. Example: 56. */
  58. stashloc character varying NULL, /* 'Storage Location'. Explanation: Where processed data is stored. Data type: VARCHAR(12). Possible categories: Local, Network, Cloud. */
  59. safebak character NULL, /* 'Backup Status'. Explanation: Indicates progress of data backup. Data type: CHAR(8). Possible categories: In Progress, Completed, Pending. */
  60. datalevel text NULL, /* 'Data Access Level'. Explanation: Access restrictions or sharing level. Data type: TEXT. Possible categories: Confidential, Public, Restricted. */
  61. metabench character varying NULL, /* 'Metadata Standard'. Explanation: Which metadata framework is applied. Data type: VARCHAR(10). Possible categories: Dublin Core, CIDOC CRM, Custom. */
  62. coordframe character varying NULL, /* 'Coordinate System'. Explanation: Defines spatial reference frame. Data type: VARCHAR(12). Possible categories: Local, WGS84, Custom. */
  63. elevref character varying NULL, /* 'Elevation Reference'. Explanation: Reference for altitude or elevation. Data type: VARCHAR(16). Possible categories: Arbitrary, Sea Level, Local. */
  64. remaingb numeric NULL, /* 'Remaining Storage (GB)'. Explanation: Remaining disk or cloud space. Data type: NUMERIC(7,2). Example: 983.5. */
  65. stationlink character NULL, /* 'Total Station Integration'. Explanation: Indicates if total station integration is used. Data type: CHAR(6). Example: 'Yes'. */
  66. camcal text NULL, /* 'Camera Calibration Status'. Explanation: Whether camera calibration is completed or pending. Data type: TEXT. Example: 'Required'. */
  67. lensdist character varying NULL, /* 'Lens Distortion Status'. Explanation: Whether lens distortion is corrected or unknown. Data type: VARCHAR(14). Possible categories: Corrected, Unknown. */
  68. colortune character NULL, /* 'Color Balance Status'. Explanation: Indicates color adjustment requirement. Data type: CHAR(10). Possible categories: Required, Adjusted. */
  69. flowstage character varying NULL, /* 'Processing Stage'. Explanation: Current stage of the processing workflow. Data type: VARCHAR(18). Possible categories: Aligned, Meshed, Final. */
  70. fmtver character NULL, /* 'Export Format Version'. Explanation: Version tag for exported files. Data type: CHAR(3). Possible categories: 2.3, 2.7, 4.0, 1.1. */
  71. PRIMARY KEY (flowregistry),
  72. FOREIGN KEY (equipref) REFERENCES equipment(equipregistry),
  73. FOREIGN KEY (zoneref) REFERENCES sites(zoneregistry)
  74. );
  75.  
  76. First 3 rows:
  77. flowregistry equipref zoneref flowsoft flowhrs proccpu memusagegb procgpu stashloc safebak datalevel metabench coordframe elevref remaingb stationlink camcal lensdist colortune flowstage fmtver
  78. -------------- ---------- --------- -------------- --------- --------- ------------ --------- ---------- ----------- ------------ ----------- ------------ --------- ---------- ------------- -------- ---------- ----------- ----------- --------
  79. 1 SN20065 SC9016 RealityCapture 21.9 81 70.3 84 Local In Progress Confidential Dublin Core Local Arbitrary 983.5 Partial Invalid Corrected Required Aligned 2.3
  80. 2 SN29799 SC9081 CloudCompare 34.9 100 13.4 56 Network Completed Public CIDOC CRM WGS84 Local 599.6 Partial Required Unknown Required Aligned 2.7
  81. 3 SN83019 SC4817 Agisoft 25.7 67 51.7 66 Network Pending Confidential CIDOC CRM Custom Sea Level 306.1 Partial Required Unknown Adjusted Meshed 4
  82. ...
  83.  
  84.  
  85. CREATE TABLE "personnel" (
  86. crewregistry character NOT NULL, /* 'Operator ID'. Explanation: Primary key for Personnel, representing an operator. Data type: CHAR(8). Example: 'OP4641'. */
  87. crewlabel character varying NULL, /* 'Operator Name'. Explanation: Name or label for the operator. Data type: VARCHAR(50). Example: 'Joel Wallace'. */
  88. leadregistry character NULL, /* 'Supervisor ID'. Explanation: Identifies a supervisor. Data type: CHAR(8). Example: 'SV7658'. */
  89. leadlabel character varying NULL, /* 'Supervisor Name'. Explanation: Name of the supervisor. Data type: VARCHAR(40). Example: 'Michael Kaiser'. */
  90. PRIMARY KEY (crewregistry)
  91. );
  92.  
  93. First 3 rows:
  94. crewregistry crewlabel leadregistry leadlabel
  95. -------------- ------------- -------------- -----------------
  96. OP4641 Joel Wallace SV7658 Michael Kaiser
  97. OP8435 Latoya Abbott SV2189 Stephanie Marquez
  98. OP7199 Aaron Knight SV6920 Victoria George
  99. ...
  100.  
  101.  
  102. CREATE TABLE "scanspatial" (
  103. domainregistry bigint NOT NULL DEFAULT nextval('scanspatial_domainregistry_seq'::regclass), /* 'Spatial Record ID'. Explanation: Primary key for spatial data. Data type: BIGSERIAL. Example: 4001. */
  104. arcref character varying NOT NULL, /* 'Project Reference'. Explanation: Project ID linking spatial data. Data type: VARCHAR(10). Example: 'PR7509'. */
  105. crewref character NOT NULL, /* 'Operator Reference'. Explanation: Operator ID for these spatial records. Data type: CHAR(8). Example: 'OP4641'. */
  106. aream2 numeric NULL, /* 'Surface Area (m²)'. Explanation: Calculated area in square meters. Data type: NUMERIC(8,3). Example: 78.01. */
  107. volm3 numeric NULL, /* 'Volume (m³)'. Explanation: Computed volume in cubic meters. Data type: NUMERIC(9,4). Example: 76.7. */
  108. boxx numeric NULL, /* 'Bounding Box X (m)'. Explanation: Size of bounding box along X-axis, in meters. Data type: NUMERIC(8,2). Example: 40.12. */
  109. boxy numeric NULL, /* 'Bounding Box Y (m)'. Explanation: Size of bounding box along Y-axis, in meters. Data type: NUMERIC(8,3). Example: 1.06. */
  110. boxz numeric NULL, /* 'Bounding Box Z (m)'. Explanation: Size of bounding box along Z-axis, in meters. Data type: NUMERIC(9,2). Example: 16.41. */
  111. angleaz real NULL, /* 'Orientation (degrees)'. Explanation: Azimuth or rotation angle around vertical axis. Data type: REAL. Example: 342.4. */
  112. angletilt double precision NULL, /* 'Tilt Angle (degrees)'. Explanation: Inclination angle from horizontal. Data type: DOUBLE PRECISION. Example: 23.9. */
  113. groundspan numeric NULL, /* 'Ground Sampling Distance (mm)'. Explanation: Effective resolution on the ground, in mm. Data type: NUMERIC(6,3). Example: 4.13. */
  114. PRIMARY KEY (domainregistry),
  115. FOREIGN KEY (arcref) REFERENCES projects(arcregistry),
  116. FOREIGN KEY (crewref) REFERENCES personnel(crewregistry)
  117. );
  118.  
  119. First 3 rows:
  120. domainregistry arcref crewref aream2 volm3 boxx boxy boxz angleaz angletilt groundspan
  121. ---------------- -------- --------- -------- ------- ------ ------ ------ --------- ----------- ------------
  122. 1 PR7509 OP4641 78.01 76.7 40.12 1.06 8.74 342.4 23.9 4.13
  123. 2 PR8078 OP8435 746.55 85.66 26.17 8.75 16.41 113 12.9 0.93
  124. 3 PR9973 OP7199 294.59 6.02 15.32 31.08 36.71 275.4 -22.1 1.12
  125. ...
  126.  
  127.  
  128. CREATE TABLE "scanqc" (
  129. qualregistry bigint NOT NULL DEFAULT nextval('scanqc_qualregistry_seq'::regclass), /* 'QC Record ID'. Explanation: Primary key for quality control records. Data type: BIGSERIAL. Example: 9001. */
  130. arcref character varying NOT NULL, /* 'Project Reference'. Explanation: Points to a project for QC. Data type: VARCHAR(10). Example: 'PR7509'. */
  131. crewref character NOT NULL, /* 'Operator Reference'. Explanation: Identifies the operator associated with QC. Data type: CHAR(8). Example: 'OP4641'. */
  132. accucheck character varying NULL, /* 'Accuracy Assessment'. Explanation: Indicates if accuracy check was done. Data type: VARCHAR(22). Possible categories: Pending, Completed, Not Required. */
  133. ctrlstate character NULL, /* 'Quality Control Status'. Explanation: State of QC checks. Data type: CHAR(10). Possible categories: Failed, Pending. */
  134. valimeth character varying NULL, /* 'Validation Method'. Explanation: How the data was validated (automated or manual). Data type: VARCHAR(18). Possible categories: Automated, Visual. */
  135. valistate text NULL, /* 'Validation Status'. Explanation: Final status of validation. Data type: TEXT. Possible categories: Rejected, Approved. */
  136. archstat character varying NULL, /* 'Archival Status'. Explanation: Indicates if data is archived. Data type: VARCHAR(10). Possible categories: Verified, Pending. */
  137. pubstat character varying NULL, /* 'Publication Status'. Explanation: Whether data is published, drafted, etc. Data type: VARCHAR(24). Possible categories: Draft, Submitted. */
  138. copystat character NULL, /* 'Copyright Status'. Explanation: Copyright or licensing restriction. Data type: CHAR(10). Possible categories: Reserved, Restricted, Open Access. */
  139. refmention character varying NULL, /* 'Data Citation'. Explanation: Reference or citation code for the data. Data type: VARCHAR(60). Possible categories: Citation-8447, Citation-6197, Citation-8090, Citation-2238. */
  140. remark text NULL, /* 'Additional Notes'. Explanation: Any free-text remarks or comments. Data type: TEXT. Example: 'Sell shoulder understand serious degree particular game.' */
  141. PRIMARY KEY (qualregistry),
  142. FOREIGN KEY (arcref) REFERENCES projects(arcregistry),
  143. FOREIGN KEY (crewref) REFERENCES personnel(crewregistry)
  144. );
  145.  
  146. First 3 rows:
  147. qualregistry arcref crewref accucheck ctrlstate valimeth valistate archstat pubstat copystat refmention remark
  148. -------------- -------- --------- ------------ ----------- ---------- ----------- ---------- --------- ----------- ------------- --------------------------------------------------------
  149. 1 PR7509 OP4641 Not Required Pending Automated Rejected Verified Draft Reserved Citation-8447
  150. 2 PR8078 OP8435 Completed Pending Visual Rejected Verified Submitted Open Access Citation-6197
  151. 3 PR9973 OP7199 Pending Failed Visual Approved Pending Draft Open Access Citation-8090 Sell shoulder understand serious degree particular game.
  152. ...
  153.  
  154.  
  155. CREATE TABLE "sites" (
  156. zoneregistry character varying NOT NULL, /* 'Site Code'. Explanation: Primary key for a site. Data type: VARCHAR(12). Example: 'SC9016'. */
  157. zonelabel text NULL, /* 'Site Name'. Explanation: Descriptive name or label for the site. Data type: TEXT. Example: 'Site-North Alexanderville'. */
  158. digunit character varying NULL, /* 'Excavation Unit'. Explanation: Designation of a specific trench or unit. Data type: VARCHAR(8). Example: 'Unit-C9'. */
  159. gridtrace character varying NULL, /* 'Grid Reference'. Explanation: Grid or coordinate notation for the site location. Data type: VARCHAR(12). Example: 'S29-E8'. */
  160. geox numeric NULL, /* 'Latitude'. Explanation: Geographic latitude in decimal degrees. Data type: NUMERIC(8,5). Example: -9.60213. */
  161. geoy numeric NULL, /* 'Longitude'. Explanation: Geographic longitude in decimal degrees. Data type: NUMERIC(8,5). Example: -2.75641. */
  162. heightm numeric NULL, /* 'Altitude (m)'. Explanation: Elevation above sea level, in meters. Data type: NUMERIC(7,1). Example: 4391.4. */
  163. depthc numeric NULL, /* 'Depth (cm)'. Explanation: Depth measurement in centimeters. Data type: NUMERIC(7,1). Example: 329.9. */
  164. phasefactor character varying NULL, /* 'Cultural/Historical Period'. Explanation: Indicates the archaeological or historical phase. Data type: VARCHAR(25). Possible categories: Iron Age, Medieval, Classical, Bronze Age. */
  165. guessdate character varying NULL, /* 'Estimated Date'. Explanation: Approximate or labeled date (BCE/CE). Data type: VARCHAR(20). Possible categories: -2929 BCE, 1335 BCE, -4985 BCE, -3387 BCE. */
  166. typesite character varying NULL, /* 'Site Type'. Explanation: Classification of the site. Data type: VARCHAR(25). Possible categories: Burial, Industrial, Military, Settlement, Religious. */
  167. presstat character varying NULL, /* 'Preservation Status'. Explanation: Condition of preservation at the site. Data type: VARCHAR(25). Possible categories: Excellent, Fair, Critical, Good, Poor. */
  168. guardhint character NULL, /* 'Weather Protection'. Explanation: Indicates if the site is protected from weather. Data type: CHAR(5). Possible categories: None, Temporary. */
  169. entrystat character varying NULL, /* 'Site Access Status'. Explanation: Access restrictions or availability of the site. Data type: VARCHAR(8). Possible categories: Closed, Restricted, Open. */
  170. saferank character varying NULL, /* 'Security Level'. Explanation: Level of security or safety at the site. Data type: VARCHAR(15). Possible categories: Minimal, High, Standard. */
  171. insurstat character varying NULL, /* 'Insurance Status'. Explanation: Whether the site is insured, pending, or expired. Data type: VARCHAR(15). Possible categories: Expired, Pending. */
  172. riskeval text NULL, /* 'Risk Assessment Status'. Explanation: Status of risk evaluation for the site. Data type: TEXT. Possible categories: Required, Completed, Pending. */
  173. healtheval character varying NULL, /* 'Health and Safety Status'. Explanation: Indicates health/safety approvals. Data type: VARCHAR(12). Possible categories: Approved, Review, Pending. */
  174. envhaz character NULL, /* 'Environmental Risk'. Explanation: Rating of environmental hazards. Data type: CHAR(6). Possible categories: Low, Medium, High. */
  175. PRIMARY KEY (zoneregistry)
  176. );
  177.  
  178. First 3 rows:
  179. zoneregistry zonelabel digunit gridtrace geox geoy heightm depthc phasefactor guessdate typesite presstat guardhint entrystat saferank insurstat riskeval healtheval envhaz
  180. -------------- ------------------------- --------- ----------- -------- --------- --------- -------- ------------- ----------- ---------- ---------- ----------- ----------- ---------- ----------- ---------- ------------ --------
  181. SC9016 Site-North Alexanderville Unit-C9 S29-E8 -9.60214 -2.75641 4391.4 329.9 Iron Age -2929 BCE Burial Excellent Closed Minimal Expired Required Approved Low
  182. SC9081 Site-Grahammouth Unit-A14 N44-W27 57.1075 70.0361 429.3 97.5 Medieval 1335 BCE Industrial Fair Temporary Restricted Standard Pending Pending Pending Low
  183. SC4817 Site-Port Brianside Unit-D19 S48-W26 73.6055 141.711 4934.6 499.9 Iron Age -4985 BCE Burial Critical Closed High Expired Completed Review High
  184. ...
  185.  
  186.  
  187. CREATE TABLE "scanfeatures" (
  188. traitregistry bigint NOT NULL DEFAULT nextval('scanfeatures_traitregistry_seq'::regclass), /* 'Feature Record ID'. Explanation: Primary key for feature data. Data type: BIGSERIAL. Example: 5001. */
  189. zoneref character varying NOT NULL, /* 'Site Reference'. Explanation: Site code tied to these features. Data type: VARCHAR(12). Example: 'SC9016'. */
  190. equipref character NOT NULL, /* 'Equipment Reference'. Explanation: Equipment ID used to detect these features. Data type: CHAR(10). Example: 'SN20065'. */
  191. traitextract character varying NULL, /* 'Feature Extraction Method'. Explanation: Method used to extract features (manual or automated). Data type: VARCHAR(25). Possible categories: Manual, Semi-automatic, Automatic. */
  192. traitcount integer NULL, /* 'Number of Detected Features'. Explanation: How many features were identified. Data type: INTEGER. Example: 516. */
  193. articount integer NULL, /* 'Artifact Count'. Explanation: Number of artifacts recognized. Data type: INTEGER. Example: 71. */
  194. structkind character varying NULL, /* 'Structure Type'. Explanation: Type of structural element. Data type: VARCHAR(15). Possible categories: Artifact, Complex, Wall, Foundation. */
  195. matkind character varying NULL, /* 'Material Type'. Explanation: Primary composition or material. Data type: VARCHAR(15). Possible categories: Organic, Metal, Mixed, Ceramic, Stone. */
  196. huestudy character varying NULL, /* 'Color Analysis'. Explanation: Status of color analysis (done or pending). Data type: VARCHAR(15). Possible categories: Partial, Completed, Not Required. */
  197. texturestudy character varying NULL, /* 'Texture Analysis'. Explanation: Status of texture analysis. Data type: VARCHAR(15). Possible categories: Partial, Completed, Not Required. */
  198. patternnote text NULL, /* 'Pattern Recognition'. Explanation: Notes regarding detected patterns. Data type: TEXT. Possible categories: Not Required, None, Partial. */
  199. PRIMARY KEY (traitregistry),
  200. FOREIGN KEY (equipref) REFERENCES equipment(equipregistry),
  201. FOREIGN KEY (zoneref) REFERENCES sites(zoneregistry)
  202. );
  203.  
  204. First 3 rows:
  205. traitregistry zoneref equipref traitextract traitcount articount structkind matkind huestudy texturestudy patternnote
  206. --------------- --------- ---------- -------------- ------------ ----------- ------------ --------- ---------- -------------- -------------
  207. 1 SC9016 SN20065 Manual 516 71 Artifact Organic Partial Partial Not Required
  208. 2 SC9081 SN29799 Semi-automatic 899 22 Complex Metal Partial Not Required Partial
  209. 3 SC4817 SN83019 Manual 103 69 Complex Ceramic Completed Partial Not Required
  210. ...
  211.  
  212.  
  213. CREATE TABLE "equipment" (
  214. equipregistry character NOT NULL, /* 'Equipment ID'. Explanation: Primary key for equipment. Data type: CHAR(10). Example: 'SN20065'. */
  215. equipform character varying NULL, /* 'Equipment Type'. Explanation: Type or category of scanning equipment. Data type: VARCHAR(28). Possible categories: LiDAR, Structured Light, Photogrammetry, Laser. */
  216. equipdesign character varying NULL, /* 'Equipment Model'. Explanation: Model name or identifier. Data type: VARCHAR(14). Example: 'Model-669'. */
  217. equiptune date NULL, /* 'Calibration Date'. Explanation: Date the equipment was last calibrated. Data type: DATE. Example: '2024-11-01'. */
  218. equipstatus character varying NULL, /* 'Equipment Condition'. Explanation: Overall condition of the equipment. Data type: VARCHAR(16). Possible categories: Excellent, Good, Fair, Poor. */
  219. powerlevel smallint NULL, /* 'Battery Level (%)'. Explanation: Battery level as an integer percentage. Data type: SMALLINT. Example: 62. */
  220. PRIMARY KEY (equipregistry)
  221. );
  222.  
  223. First 3 rows:
  224. equipregistry equipform equipdesign equiptune equipstatus powerlevel
  225. --------------- ---------------- ------------- ----------- ------------- ------------
  226. SN20065 LiDAR Model-669 2024-11-01 Excellent 62
  227. SN29799 Structured Light Model-835 2024-09-09 Good 21
  228. SN83019 Photogrammetry Model-566 2025-02-08 Good 46
  229. ...
  230.  
  231.  
  232. CREATE TABLE "scanconservation" (
  233. cureregistry bigint NOT NULL DEFAULT nextval('scanconservation_cureregistry_seq'::regclass), /* 'Conservation Record ID'. Explanation: Primary key for conservation data. Data type: BIGSERIAL. Example: 6001. */
  234. arcref character varying NOT NULL, /* 'Project Reference'. Explanation: Associates conservation data with a project. Data type: VARCHAR(10). Example: 'PR7509'. */
  235. zoneref character varying NOT NULL, /* 'Site Reference'. Explanation: Associates conservation data with a site code. Data type: VARCHAR(12). Example: 'SC9016'. */
  236. harmassess character varying NULL, /* 'Damage Assessment'. Explanation: Indicates severity of damage. Data type: VARCHAR(15). Possible categories: None, Moderate, Severe. */
  237. curerank character varying NULL, /* 'Conservation Priority'. Explanation: Priority level for conservation efforts. Data type: VARCHAR(15). Possible categories: Critical, Low, High. */
  238. structstate character varying NULL, /* 'Structural Stability'. Explanation: Stability level of the structure. Data type: VARCHAR(15). Possible categories: Stable, Moderate, Unstable. */
  239. intervhistory text NULL, /* 'Intervention History'. Explanation: Past restoration or intervention records. Data type: TEXT. Possible categories: None, Major, Minor. */
  240. priordocs text NULL, /* 'Previous Documentation'. Explanation: Level of existing documentation. Data type: TEXT. Possible categories: None, Partial, Complete. */
  241. PRIMARY KEY (cureregistry),
  242. FOREIGN KEY (arcref) REFERENCES projects(arcregistry),
  243. FOREIGN KEY (zoneref) REFERENCES sites(zoneregistry)
  244. );
  245.  
  246. First 3 rows:
  247. cureregistry arcref zoneref harmassess curerank structstate intervhistory priordocs
  248. -------------- -------- --------- ------------ ---------- ------------- --------------- -----------
  249. 1 PR7509 SC9016 Critical Moderate Minor
  250. 2 PR8078 SC9081 Severe Low Moderate Major Partial
  251. 3 PR9973 SC4817 Moderate High Moderate
  252. ...
  253.  
  254.  
  255. CREATE TABLE "scans" (
  256. questregistry character varying NOT NULL, /* 'Scan ID'. Explanation: Primary key identifying a particular scan. Data type: VARCHAR(16). Example: 'ASD409481'. */
  257. chronotag timestamp without time zone NULL, /* 'Scan Timestamp'. Explanation: Timestamp representing the scan date/time. Data type: TIMESTAMP. If stored as a numeric like 44302.55648, it may be an Excel serial date/time (days since 1900-01-00). Example: 44302.55648. */
  258. arcref character varying NOT NULL, /* 'Project Reference'. Explanation: Links a scan to a specific project. Data type: VARCHAR(10). Example: 'PR7509'. */
  259. crewref character NOT NULL, /* 'Operator Reference'. Explanation: Records which operator performed the scan. Data type: CHAR(8). Example: 'OP4641'. */
  260. zoneref character varying NOT NULL, /* 'Site Reference'. Explanation: Associates the scan with a site code. Data type: VARCHAR(12). Example: 'SC9016'. */
  261. scancount smallint NULL, /* 'Number of Scans'. Explanation: Numeric count of scans in a set. Data type: SMALLINT. Example: 5. */
  262. climtune character varying NULL, /* 'Weather Conditions'. Explanation: Indicates the weather during scanning. Data type: VARCHAR(22). Possible categories: Windy, Rainy, Cloudy, Clear. */
  263. huecatch character varying NULL, /* 'Color Capture'. Explanation: Mode of color recording. Data type: VARCHAR(10). Possible categories: RGB, Grayscale, None. */
  264. fmtfile character NULL, /* 'File Format'. Explanation: Format in which scan data is saved. Data type: CHAR(4). Possible categories: PTS, PLY, OBJ, LAS, E57. */
  265. gbsize numeric NULL, /* 'File Size (GB)'. Explanation: Size of the scan file in gigabytes. Data type: NUMERIC(5,2). Example: 24.71. */
  266. pressratio numeric NULL, /* 'Compression Ratio'. Explanation: Compression level applied to scan data. Data type: NUMERIC(4,2). Example: 3.22. */
  267. spanmin numeric NULL, /* 'Scan Duration (min)'. Explanation: Duration of the scanning process, in minutes. Data type: NUMERIC(5,2). Example: 63. */
  268. PRIMARY KEY (questregistry),
  269. FOREIGN KEY (arcref) REFERENCES projects(arcregistry),
  270. FOREIGN KEY (crewref) REFERENCES personnel(crewregistry),
  271. FOREIGN KEY (zoneref) REFERENCES sites(zoneregistry)
  272. );
  273.  
  274. First 3 rows:
  275. questregistry chronotag arcref crewref zoneref scancount climtune huecatch fmtfile gbsize pressratio spanmin
  276. --------------- -------------------------- -------- --------- --------- ----------- ---------- ---------- --------- -------- ------------ ---------
  277. ASD409481 2024-09-03 07:20:28.479288 PR7509 OP4641 SC9016 5 Windy RGB PTS 24.71 3.22 63
  278. ASD648638 2024-07-27 08:52:12.479479 PR8078 OP8435 SC9081 2 Rainy RGB PLY 21.63 6.86 240
  279. ASD535327 2025-01-24 12:45:10.479479 PR9973 OP7199 SC4817 7 Windy RGB PLY 41.48 4.2 37
  280. ...
  281.  
  282.  
  283. CREATE TABLE "scanregistration" (
  284. logregistry bigint NOT NULL DEFAULT nextval('scanregistration_logregistry_seq'::regclass), /* 'Registration Record ID'. Explanation: Primary key for registration logs. Data type: BIGSERIAL. Example: 7001. */
  285. crewref character NOT NULL, /* 'Operator Reference'. Explanation: Operator ID who performed registration. Data type: CHAR(8). Example: 'OP4641'. */
  286. arcref character varying NOT NULL, /* 'Project Reference'. Explanation: Project ID linked to registration data. Data type: VARCHAR(10). Example: 'PR7509'. */
  287. logaccumm numeric NULL, /* 'Registration Accuracy (mm)'. Explanation: Accuracy of the registration in mm. Data type: NUMERIC(5,3). Example: 0.84. */
  288. refmark character varying NULL, /* 'Reference Markers'. Explanation: Numeric codes for registration targets. Data type: VARCHAR(6). Possible categories: 40, 31, 25, 21. */
  289. ctrlpts character varying NULL, /* 'Control Points'. Explanation: Numeric codes for control points. Data type: VARCHAR(6). Possible categories: 73, 99, 6, 84. */
  290. logmethod character varying NULL, /* 'Registration Method'. Explanation: Method for aligning scans. Data type: VARCHAR(15). Possible categories: Target-based, Hybrid, Automatic. */
  291. transform character varying NULL, /* 'Transformation Matrix'. Explanation: Identifier for the transform matrix used. Data type: VARCHAR(15). Possible categories: Matrix-47, Matrix-113, Matrix-543. */
  292. errscale character varying NULL, /* 'Error Metrics'. Explanation: Type of error measurement. Data type: VARCHAR(20). Possible categories: Cloud-to-Mesh, Cloud-to-Cloud, RMSE. */
  293. errvalmm numeric NULL, /* 'Error Value (mm)'. Explanation: Measured error in millimeters. Data type: NUMERIC(6,3). Example: 6.962. */
  294. PRIMARY KEY (logregistry),
  295. FOREIGN KEY (arcref) REFERENCES projects(arcregistry),
  296. FOREIGN KEY (crewref) REFERENCES personnel(crewregistry)
  297. );
  298.  
  299. First 3 rows:
  300. logregistry crewref arcref logaccumm refmark ctrlpts logmethod transform errscale errvalmm
  301. ------------- --------- -------- ----------- --------- --------- ------------ ----------- -------------- ----------
  302. 1 OP4641 PR7509 0.84 40 73 Hybrid Matrix-47 Cloud-to-Mesh 6.962
  303. 2 OP8435 PR8078 3.44 21 6 Target-based Matrix-712 Cloud-to-Mesh 4.442
  304. 3 OP7199 PR9973 3.95 25 84 Hybrid Matrix-113 Cloud-to-Cloud 6.069
  305. ...
  306.  
  307.  
  308. CREATE TABLE "scanenvironment" (
  309. airregistry bigint NOT NULL DEFAULT nextval('scanenvironment_airregistry_seq'::regclass), /* 'Environment Record ID'. Explanation: Primary key for environment data. Data type: BIGSERIAL. Example: 1001. */
  310. zoneref character varying NOT NULL, /* 'Site Reference'. Explanation: Links environment conditions to a specific site code. Data type: VARCHAR(12). Example: 'SC9016'. */
  311. equipref character NOT NULL, /* 'Equipment Reference'. Explanation: Identifies which equipment these environment data refer to. Data type: CHAR(10). Example: 'SN20065'. */
  312. ambictemp numeric NULL, /* 'Ambient Temperature (C)'. Explanation: Air temperature in Celsius. Data type: NUMERIC(5,2). Example: 25.3. */
  313. humepct numeric NULL, /* 'Relative Humidity (%)'. Explanation: Humidity as a percentage. Data type: NUMERIC(5,2). Example: 60.4. */
  314. illumelux integer NULL, /* 'Light Conditions (lux)'. Explanation: Measured illumination in lux. Data type: INTEGER. Example: 86054. */
  315. geosignal character varying NULL, /* 'GPS Signal Quality'. Explanation: Quality rating for GPS reception. Data type: VARCHAR(15). Possible categories: None, Poor, Good, Excellent. */
  316. trackstatus character varying NULL, /* 'RTK Status'. Explanation: Real-Time Kinematic correction state. Data type: VARCHAR(12). Possible categories: None, Fixed. */
  317. linkstatus character varying NULL, /* 'Network Status'. Explanation: Status of network connectivity. Data type: VARCHAR(12). Possible categories: Disconnected, Connected. */
  318. photomap character NULL, /* 'Photogrammetry Overlap'. Explanation: Percentage overlap for photogrammetry images. Data type: CHAR(4). Possible categories: 80%, 60%, 90%. */
  319. imgcount smallint NULL, /* 'Number of Images'. Explanation: How many images were taken for photogrammetry. Data type: SMALLINT. Example: 248. */
  320. PRIMARY KEY (airregistry),
  321. FOREIGN KEY (equipref) REFERENCES equipment(equipregistry),
  322. FOREIGN KEY (zoneref) REFERENCES sites(zoneregistry)
  323. );
  324.  
  325. First 3 rows:
  326. airregistry zoneref equipref ambictemp humepct illumelux geosignal trackstatus linkstatus photomap imgcount
  327. ------------- --------- ---------- ----------- --------- ----------- ----------- ------------- ------------ ---------- ----------
  328. 1 SC9016 SN20065 25.3 60.4 86054 Disconnected 80% 248
  329. 2 SC9081 SN29799 8.8 32.9 51271 Connected 60% 874
  330. 3 SC4817 SN83019 -3.9 72.6 21889 Poor Fixed Disconnected 80% 750
  331. ...
  332.  
  333.  
  334. CREATE TABLE "scanpointcloud" (
  335. cloudregistry bigint NOT NULL DEFAULT nextval('scanpointcloud_cloudregistry_seq'::regclass), /* 'Point Cloud Record ID'. Explanation: Primary key for point cloud data. Data type: BIGSERIAL. Example: 2001. */
  336. crewref character NOT NULL, /* 'Operator Reference'. Explanation: Which operator is linked to this point cloud. Data type: CHAR(8). Example: 'OP4641'. */
  337. arcref character varying NOT NULL, /* 'Project Reference'. Explanation: Project ID associated with this point cloud. Data type: VARCHAR(10). Example: 'PR7509'. */
  338. scanresolmm numeric NULL, /* 'Scan Resolution (mm)'. Explanation: Resolution of points in millimeters. Data type: NUMERIC(5,2). Example: 2.4. */
  339. pointdense integer NULL, /* 'Point Density (points/m²)'. Explanation: Density of points per square meter. Data type: INTEGER. Example: 42812. */
  340. coverpct numeric NULL, /* 'Coverage (%)'. Explanation: Surface coverage percentage. Data type: NUMERIC(4,1). Example: 91.2. */
  341. totalpts bigint NULL, /* 'Total Points'. Explanation: Overall number of points in the cloud. Data type: BIGINT. Example: 46562436. */
  342. clouddense integer NULL, /* 'Point-Cloud Density Code'. Explanation: A numeric code classifying point density. Data type: INTEGER. Possible categories: 9449, 431, 7553, 1746. */
  343. lappct numeric NULL, /* 'Overlap (%)'. Explanation: Overlap percentage among multiple scans. Data type: NUMERIC(4,1). Example: 31.3. */
  344. noisedb numeric NULL, /* 'Noise Level (dB)'. Explanation: Measured noise in decibels. Data type: NUMERIC(6,3). Example: 1.318. */
  345. refpct numeric NULL, /* 'Surface Reflectivity (%)'. Explanation: Reflectivity percentage of scanned surfaces. Data type: NUMERIC(4,1). Example: 65.4. */
  346. PRIMARY KEY (cloudregistry),
  347. FOREIGN KEY (arcref) REFERENCES projects(arcregistry),
  348. FOREIGN KEY (crewref) REFERENCES personnel(crewregistry)
  349. );
  350.  
  351. First 3 rows:
  352. cloudregistry crewref arcref scanresolmm pointdense coverpct totalpts clouddense lappct noisedb refpct
  353. --------------- --------- -------- ------------- ------------ ---------- ---------- ------------ -------- --------- --------
  354. 1 OP4641 PR7509 2.4 42812 91.2 46562436 9449 31.3 1.318 11
  355. 2 OP8435 PR8078 4.72 974154 84.7 73534012 431 48.6 1.788 65.4
  356. 3 OP7199 PR9973 4.9 934361 98.1 87734478 1746 31.7 1.79 53.8
  357. ...
  358. </СХЕМА БАЗЫ ДАННЫХ>
  359. <СЛОВАРЬ ЗНАНИЙ, СВЯЗАННЫЙ С БАЗОЙ ДАННЫХ>
  360. {"id": 0, "knowledge": "Scan Resolution Index (SRI)", "description": "A sophisticated compound index measuring the overall resolution quality of a scan based on resolution and point density.", "definition": "SRI = \\frac{\\log_{10}(ScanResolMm \\times 10^3)}{\\log_{10}(PointDense)} \\times 5, \\text{ where lower values indicate higher quality resolution and more balanced scanning parameters.}", "type": "calculation_knowledge", "children_knowledge": -1}
  361. {"id": 1, "knowledge": "Scan Coverage Effectiveness (SCE)", "description": "Measures how effectively a scan covers its target area considering both coverage percentage and overlap redundancy.", "definition": "SCE = CoverPct \\times \\left(1 + \\frac{LapPct}{100} \\times \\left(1 - \\frac{CoverPct}{100}\\right)\\right), \\text{ where higher values indicate more effective coverage with appropriate overlap.}", "type": "calculation_knowledge", "children_knowledge": -1}
  362. {"id": 2, "knowledge": "Point Cloud Density Ratio (PCDR)", "description": "Evaluates the relationship between total points and cloud density, used to assess scan efficiency and data distribution.", "definition": "PCDR = \\frac{TotalPts}{CloudDense \\times AreaM2}, \\text{ where higher values suggest more efficient and spatially consistent scanning techniques.}", "type": "calculation_knowledge", "children_knowledge": -1}
  363. {"id": 3, "knowledge": "Scan Quality Score (SQS)", "description": "Comprehensive quality metric combining resolution, coverage, and noise factors with weighted importance.", "definition": "SQS = \\left(\\frac{10}{SRI}\\right)^{1.5} \\times \\left(\\frac{SCE}{100}\\right) \\times \\left(1 - \\frac{NoiseDb}{30}\\right)^2, \\text{ where higher values indicate exponentially better overall scan quality with emphasis on resolution.}", "type": "calculation_knowledge", "children_knowledge": [0, 1]}
  364. {"id": 4, "knowledge": "Mesh Complexity Ratio (MCR)", "description": "Measures the topological complexity of a mesh relative to its resolution, helping identify overly complex or simplified archaeological models.", "definition": "MCR = \\frac{FacetFaces}{FacetVerts \\times FacetResMm^2} \\times 10^3, \\text{ where higher values indicate more complex meshes for a given resolution, capturing finer archaeological details.}", "type": "calculation_knowledge", "children_knowledge": -1}
  365. {"id": 5, "knowledge": "Texture Density Index (TDI)", "description": "Evaluates the pixel density of textures relative to mesh resolution for assessing surface detail preservation.", "definition": "TDI = \\frac{TexPix}{\\sqrt{FacetFaces} \\times FacetResMm} \\times 10^{-2}, \\text{ where higher values indicate more detailed textures relative to geometric complexity.}", "type": "calculation_knowledge", "children_knowledge": -1}
  366. {"id": 6, "knowledge": "Model Fidelity Score (MFS)", "description": "Combines mesh complexity, texture quality, and geometric accuracy to assess overall 3D model fidelity for archaeological analysis.", "definition": "MFS = MCR \\times \\left(\\frac{TDI}{10}\\right) \\times \\left(1 + \\exp\\left(-GeomDeltaMm\\right)\\right), \\text{ where higher values indicate more accurate and detailed models with appropriate complexity.}", "type": "calculation_knowledge", "children_knowledge": [4, 5]}
  367. {"id": 7, "knowledge": "Environmental Suitability Index (ESI)", "description": "Evaluates how suitable environmental conditions were for scanning operations using weighted parameters.", "definition": "ESI = 100 - 2.5 \\times \\left|AmbicTemp - 20\\right| - \\left|\\frac{HumePct - 50}{2}\\right|^{1.5} - \\frac{600}{IllumeLux + 100}, \\text{ where higher values indicate more ideal scanning conditions adjusted for relative importance.}", "type": "calculation_knowledge", "children_knowledge": -1}
  368. {"id": 8, "knowledge": "Processing Efficiency Ratio (PER)", "description": "Measures the efficiency of scan processing by comparing processing time to data complexity and size.", "definition": "PER = \\frac{GBSize \\times \\log_{10}(TotalPts)}{FlowHrs \\times (ProcCPU + ProcGPU)/200}, \\text{ where higher values indicate more efficient processing relative to data complexity.}", "type": "calculation_knowledge", "children_knowledge": -1}
  369. {"id": 9, "knowledge": "Archaeological Documentation Completeness (ADC)", "description": "Comprehensive score for how completely a site has been documented through scanning with weighted importance factors.", "definition": "ADC = \\left(SQS \\times 0.4\\right) + \\left(MFS \\times 0.4\\right) + \\left(SCE \\times 0.2\\right) - 5 \\times \\sqrt{\\frac{NoiseDb}{10}}, \\text{ where higher values indicate more complete documentation with multiple quality factors.}", "type": "calculation_knowledge", "children_knowledge": [3, 6, 1]}
  370. {"id": 10, "knowledge": "High Resolution Scan", "description": "Defines what constitutes a high-resolution archaeological scan based on quantitative parameters.", "definition": "A scan with ScanResolMm \\leq 1.0 and PointDense \\geq 1000, allowing for sub-millimeter precision in archaeological documentation and feature detection.", "type": "domain_knowledge", "children_knowledge": -1}
  371. {"id": 11, "knowledge": "Comprehensive Coverage", "description": "Defines the standard for comprehensive scan coverage of an archaeological site or artifact with statistical confidence.", "definition": "A scan with CoverPct \\geq 95 and LapPct \\geq 30, ensuring minimal data gaps and sufficient overlap for accurate registration with 95% confidence interval for spatial measurements.", "type": "domain_knowledge", "children_knowledge": -1}
  372. {"id": 12, "knowledge": "Premium Quality Scan", "description": "Defines the criteria for a premium quality archaeological scan suitable for conservation planning and scholarly publication.", "definition": "A scan that is both a High Resolution Scan and has Comprehensive Coverage with SQS > 7.5, where SQS is the Scan Quality Score, producing data suitable for detailed analysis and conservation planning.", "type": "domain_knowledge", "children_knowledge": [10, 11, 3]}
  373. {"id": 13, "knowledge": "High Fidelity Mesh", "description": "Defines criteria for high-fidelity 3D mesh models in archaeological documentation suitable for analytical studies.", "definition": "A mesh with MCR > 5.0, FacetResMm < 1.0, and GeomDeltaMm < 0.5, where MCR is the Mesh Complexity Ratio, capable of representing fine archaeological details and surface morphology.", "type": "domain_knowledge", "children_knowledge": [4]}
  374. {"id": 14, "knowledge": "Degradation Risk Zone", "description": "Identifies archaeological sites at risk of degradation requiring urgent conservation intervention based on multiple factors.", "definition": "A site with PresStat containing 'Poor' or 'Critical' and StructState not containing 'Stable', signaling immediate conservation needs due to active deterioration processes.", "type": "domain_knowledge", "children_knowledge": [26]}
  375. {"id": 15, "knowledge": "Optimal Scanning Conditions", "description": "Defines the environmental conditions considered optimal for archaeological scanning based on instrument sensitivity profiles.", "definition": "Conditions with ESI > 85, where ESI is the Environmental Suitability Index (knowledge #7), characterized by moderate temperature, humidity around 50%, and good illumination, minimizing environmental interference with scanning accuracy.", "type": "domain_knowledge", "children_knowledge": [7]}
  376. {"id": 16, "knowledge": "Digital Conservation Priority", "description": "Classification system for prioritizing digital conservation efforts based on site conditions, historical significance, and preservation status.", "definition": "A scoring system where sites in Degradation Risk Zones with GuessDate older than 1000 BCE or with TypeSite = 'Rare' or 'Unique' receive highest priority for digital preservation through Premium Quality Scans, requiring immediate allocation of scanning resources.", "type": "domain_knowledge", "children_knowledge": [12, 14]}
  377. {"id": 17, "knowledge": "Processing Bottleneck", "description": "Identifies processing workflows that are experiencing resource constraints using performance metrics.", "definition": "A processing record with PER < 0.5, where PER is the Processing Efficiency Ratio, indicating potential hardware limitations affecting processing speed and output quality, requiring workflow optimization.", "type": "domain_knowledge", "children_knowledge": [8]}
  378. {"id": 18, "knowledge": "Registration Quality Threshold", "description": "Defines the quality threshold for scan registration in archaeological documentation based on error propagation analysis.", "definition": "A registration with LogAccuMm < 1.0 and ErrValMm < 2.0, ensuring sufficient accuracy for reliable spatial analysis with maximum tolerable error below the significant feature size threshold.", "type": "domain_knowledge", "children_knowledge": -1}
  379. {"id": 19, "knowledge": "Full Archaeological Digital Twin", "description": "Defines the comprehensive digital representation of an archaeological site meeting all quality standards for research and preservation.", "definition": "A site with Premium Quality Scans, High Fidelity Mesh, Registration Quality Threshold met, and ADC > 85, where ADC is Archaeological Documentation Completeness, representing a complete digital twin suitable for research, conservation, and visualization purposes.", "type": "domain_knowledge", "children_knowledge": [12, 13, 18, 9]}
  380. {"id": 20, "knowledge": "ScanResolMm (Scan Resolution)", "description": "Illustrates the significance of scan resolution measurements in archaeological scanning for feature detection.", "definition": "Measured in millimeters, representing the smallest feature that can be distinguished in the scan. Values like 0.5mm enable documentation of fine tool marks on artifacts, while 2.0mm might only capture general shape and macroscopic features.", "type": "value_illustration", "children_knowledge": -1}
  381. {"id": 21, "knowledge": "PointDense (Point Density)", "description": "Illustrates the significance of point density in archaeological point clouds for information richness.", "definition": "Measured as points per square meter. Values around 100 capture basic site topography, 1,000 can document structural details, while 10,000+ enables analysis of surface textures and fine engravings across multiple scales of inquiry.", "type": "value_illustration", "children_knowledge": -1}
  382. {"id": 22, "knowledge": "NoiseDb (Noise Level)", "description": "Illustrates the impact of noise levels in point cloud data on feature recognition accuracy.", "definition": "Measured in decibels, representing signal-to-noise ratio in scan data. Values below 1.0 indicate clean data suitable for detailed analysis, while values above 3.0 suggest significant noise that may obscure small features and introduce measurement uncertainty.", "type": "value_illustration", "children_knowledge": -1}
  383. {"id": 23, "knowledge": "CoverPct (Coverage Percentage)", "description": "Illustrates the significance of coverage percentage in archaeological scans for site completeness assessment.", "definition": "Percentage of target area successfully captured in scan data. Values above 95% indicate near-complete documentation, while 80% might have significant gaps requiring additional scanning or interpolation methods for comprehensive site analysis.", "type": "value_illustration", "children_knowledge": -1}
  384. {"id": 24, "knowledge": "GeomDeltaMm (Geometric Accuracy)", "description": "Illustrates the significance of geometric accuracy in 3D models for measurement reliability.", "definition": "Measured in millimeters, representing the average deviation between the scan data and final 3D model. Values below 0.1mm indicate museum-quality accuracy, while values around 1.0mm are suitable for general documentation but introduce uncertainty in fine feature analysis.", "type": "value_illustration", "children_knowledge": -1}
  385. {"id": 25, "knowledge": "PhaseFactor (Cultural Period)", "description": "Illustrates the significance of cultural period classification in archaeological sites.", "definition": "Classifies archaeological sites into standardized chronological/cultural periods. Values like 'Neolithic' (10,000-4,500 BCE), 'Bronze Age' (3,300-1,200 BCE), 'Roman' (27 BCE-476 CE), or 'Medieval' (476-1453 CE) determine applicable research methodologies, conservation approaches, and contextual interpretation frameworks.", "type": "value_illustration", "children_knowledge": -1}
  386. {"id": 26, "knowledge": "StructState (Structural State)", "description": "Illustrates structural state classifications in archaeological conservation.", "definition": "A categorical assessment with specific values: 'Stable' indicates structures that maintain integrity under normal conditions, 'Unstable' indicates structures showing signs of deterioration requiring intervention, and 'Critical' indicates structures at imminent risk of collapse requiring emergency stabilization.", "type": "value_illustration", "children_knowledge": -1}
  387. {"id": 27, "knowledge": "FlowStage (Processing Stage)", "description": "Illustrates the progression of data processing in archaeological scanning workflows.", "definition": "A sequential classification system with defined stages: 'Raw' (unprocessed scan data), 'Aligned' (multiple scans registered together), 'Cleaned' (noise and artifacts removed), 'Meshed' (point cloud converted to polygon mesh), and 'Textured' (surface textures applied to mesh). Each stage represents a discrete processing milestone.", "type": "value_illustration", "children_knowledge": -1}
  388. {"id": 28, "knowledge": "LogMethod (Registration Method)", "description": "Illustrates different scan registration methodologies in archaeological documentation.", "definition": "A categorization of alignment techniques with specific methodologies: 'ICP' (Iterative Closest Point algorithm for point cloud alignment), 'Target-based' (alignment using physical reference markers), 'Hybrid' (combination of automatic and manual alignment), and 'SLAM' (Simultaneous Localization and Mapping for real-time registration). Each method has distinct accuracy characteristics and use cases.", "type": "value_illustration", "children_knowledge": -1}
  389. {"id": 29, "knowledge": "GuessDate (Estimated Dating)", "description": "Illustrates dating conventions in archaeological classification for chronological placement.", "definition": "Values like '3500-3000 BCE', '1st c. CE', or 'ca. 1450 CE' represent estimated chronological placement based on excavation findings. Precision varies from specific years to century-level estimates depending on available evidence and dating methodologies employed.", "type": "value_illustration", "children_knowledge": -1}
  390. {"id": 30, "knowledge": "Scan Time Efficiency (STE)", "description": "Measures how efficiently scanning time was used relative to data quality and completeness metrics.", "definition": "STE = \\frac{SQS \\times \\sqrt{CoverPct}}{SpanMin \\times \\sqrt{ScanCount}}, \\text{ where SQS is the Scan Quality Score and higher STE values indicate more efficient use of scanning time relative to coverage achieved.}", "type": "calculation_knowledge", "children_knowledge": [3]}
  391. {"id": 31, "knowledge": "Environmental Impact Factor (EIF)", "description": "Quantifies how environmental conditions affected scan quality using statistical correlation analysis.", "definition": "EIF = \\frac{SQS}{\\text{ESI} + 10} \\times 100, \\text{ where SQS is the Scan Quality Score and ESI is the Environmental Suitability Index. Values closer to 100 indicate minimal environmental interference with data acquisition.}", "type": "calculation_knowledge", "children_knowledge": [3, 7]}
  392. {"id": 32, "knowledge": "Feature Extraction Efficiency (FEE)", "description": "Measures the efficiency of feature identification in scan data relative to point cloud density and complexity.", "definition": "FEE = \\frac{TraitCount + ArtiCount}{PCDR \\times \\sqrt{CloudDense}} \\times 10^3, \\text{ where PCDR is the Point Cloud Density Ratio and higher values indicate more effective feature extraction from point cloud data relative to spatial distribution efficiency.}", "type": "calculation_knowledge", "children_knowledge": [2]}
  393. {"id": 33, "knowledge": "Registration Accuracy Ratio (RAR)", "description": "Evaluates registration accuracy relative to scan resolution using propagation of uncertainty principles.", "definition": "RAR = \\frac{ScanResolMm}{LogAccuMm \\times \\sqrt{1 + \\frac{ErrValMm}{LogAccuMm}}}, \\text{ where values > 1 indicate registration accuracy exceeds scan resolution, a desirable outcome for precise spatial analysis.}", "type": "calculation_knowledge", "children_knowledge": -1}
  394. {"id": 34, "knowledge": "Spatial Density Index (SDI)", "description": "Assesses point cloud density relative to site dimensions for spatial sampling adequacy.", "definition": "SDI = \\frac{TotalPts}{AreaM2 \\times 10^4} \\times \\left(\\frac{PointDense}{CloudDense}\\right)^{0.5}", "type": "calculation_knowledge", "children_knowledge": -1}
  395. {"id": 35, "knowledge": "Conservation Priority Index (CPI)", "description": "Quantifies the urgency of conservation efforts based on site condition, historical significance and structural stability.", "definition": "CPI = \\begin{cases} 100 - PS + AF \\times \\left(1 + \\frac{TS}{10}\\right), & \\text{if in a Degradation Risk Zone} \\\\ 50 - PS + AF \\times \\left(1 + \\frac{TS}{20}\\right), & \\text{otherwise} \\end{cases}, \\text{ where PS is 0-100 based on PresStat condition ('Excellent'=10, 'Good'=30, 'Fair'=50, 'Poor'=70, 'Critical'=90), AF is approximate age in millennia derived from GuessDate, and TS is 0-10 based on TypeSite rarity.}", "type": "calculation_knowledge", "children_knowledge": [14, 29]}
  396. {"id": 36, "knowledge": "Mesh-to-Point Ratio (MPR)", "description": "Evaluates the efficiency of mesh generation from point cloud data for optimal decimation determination.", "definition": "MPR = \\frac{FacetVerts}{TotalPts} \\times 100 \\times \\left(\\frac{MCR}{10}\\right)^{0.3}, \\text{ where MCR is the Mesh Complexity Ratio and values around 25-30 indicate optimal decimation for archaeological purposes with appropriate feature preservation.}", "type": "calculation_knowledge", "children_knowledge": [4]}
  397. {"id": 37, "knowledge": "Processing Resource Utilization (PRU)", "description": "Measures the efficiency of computing resource utilization during scan processing relative to data complexity.", "definition": "PRU = \\frac{FlowHrs \\times (ProcCPU + ProcGPU) / 2}{GBSize \\times 10 \\times \\log_{10}(FacetVerts + 10^4)}, \\text{ where lower values indicate more efficient use of computing resources relative to mesh complexity.}", "type": "calculation_knowledge", "children_knowledge": -1}
  398. {"id": 38, "knowledge": "Digital Preservation Quality (DPQ)", "description": "Comprehensive metric for evaluating digital preservation quality for archaeological sites with weighted quality factors.", "definition": "DPQ = (0.3 \\times ADC) + (0.3 \\times MFS) + (0.2 \\times RAR) + (0.2 \\times SCE) - 2 \\times \\sqrt{\\frac{ErrValMm}{ScanResolMm}}, \\text{ where ADC is Archaeological Documentation Completeness, MFS is Model Fidelity Score, RAR is Registration Accuracy Ratio, and SCE is Scan Coverage Effectiveness.}", "type": "calculation_knowledge", "children_knowledge": [9, 6, 33, 1]}
  399. {"id": 39, "knowledge": "Equipment Effectiveness Ratio (EER)", "description": "Evaluates how effectively equipment was utilized based on power consumption and scan quality relative to equipment capability.", "definition": "EER = \\frac{SQS \\times EquipStatus\\_value}{PowerLevel \\times (101 - EquipAge\\_days) / 365} \\times 25, \\text{ where SQS is the Scan Quality Score, EquipStatus_value is 1.0 for 'Excellent' to 0.2 for 'Poor', and EquipAge_days is days since EquipTune, with higher values indicating more efficient use of equipment relative to condition.}", "type": "calculation_knowledge", "children_knowledge": [3]}
  400. {"id": 40, "knowledge": "Spatially Complex Site", "description": "Defines sites with complex spatial characteristics requiring specialized scanning approaches based on dimensional analysis.", "definition": "A site with AreaM2 > 100 and SDI > 50, where SDI is the Spatial Density Index, requiring strategic planning for comprehensive documentation with multiple scanning stations and methodologies to capture complex spatial relationships.", "type": "domain_knowledge", "children_knowledge": [34]}
  401. {"id": 41, "knowledge": "Texture-Critical Artifact", "description": "Identifies artifacts where texture documentation is critical for analysis based on surface morphology characteristics.", "definition": "Features with TextureStudy containing 'Detailed' or 'Critical' and TDI > 8.0, where TDI is the Texture Density Index, requiring specialized imaging techniques such as photometric stereo or multi-spectral imaging for complete surface characterization.", "type": "domain_knowledge", "children_knowledge": [5]}
  402. {"id": 42, "knowledge": "Conservation Emergency", "description": "Identifies sites requiring immediate conservation intervention based on multiple risk factors and structural assessment.", "definition": "A site that is in a Degradation Risk Zone with CPI > 75, where CPI is the Conservation Priority Index, requiring immediate protective measures and priority documentation with at least Premium Quality Scans before any intervention to establish baseline condition.", "type": "domain_knowledge", "children_knowledge": [14, 35]}
  403. {"id": 43, "knowledge": "Processing Optimized Workflow", "description": "Defines optimized processing workflows balancing quality and resource use through benchmarked performance metrics.", "definition": "A processing workflow with PRU < 5.0 while maintaining MFS > 7.0, where PRU is Processing Resource Utilization and MFS is Model Fidelity Score, representing an efficient balance of resource use and output quality through optimized algorithm selection and hardware allocation.", "type": "domain_knowledge", "children_knowledge": [37, 6]}
  404. {"id": 44, "knowledge": "Registration Confidence Level", "description": "Classification system for registration confidence based on multiple factors and error propagation analysis.", "definition": "A classification where 'High Confidence' registrations have RAR > 1.5 and LogMethod containing 'Target', where RAR is Registration Accuracy Ratio, 'Medium Confidence' have RAR between 1.0-1.5, and 'Low Confidence' have RAR < 1.0, determining appropriate use cases for spatial analysis and interpretive visualization.", "type": "domain_knowledge", "children_knowledge": [33]}
  405. {"id": 45, "knowledge": "Environmental Challenge Scan", "description": "Identifies scans conducted under challenging environmental conditions requiring expertise and specialized equipment adaptation.", "definition": "A scan with EIF > 120, where EIF is Environmental Impact Factor, indicating successful data capture despite suboptimal environmental conditions through adaptive scanning methodologies and operator expertise in field condition compensation.", "type": "domain_knowledge", "children_knowledge": [31]}
  406. {"id": 46, "knowledge": "High Temporal Value Site", "description": "Identifies sites with exceptional historical significance based on age and context for prioritized research attention.", "definition": "A site with GuessDate containing dates before 500 CE and CPI > 60, where CPI is Conservation Priority Index, representing locations of exceptional chronological significance requiring specialized documentation protocols to capture temporally significant features.", "type": "domain_knowledge", "children_knowledge": [35, 29]}
  407. {"id": 47, "knowledge": "Resource-Intensive Model", "description": "Identifies 3D models requiring substantial computing resources for visualization and analysis based on complexity metrics.", "definition": "A model with FacetFaces > 2,000,000 and MPR < 15, where MPR is Mesh-to-Point Ratio, requiring specialized hardware for effective interaction and analytical software optimized for large-scale geometric processing with hierarchical level-of-detail implementation.", "type": "domain_knowledge", "children_knowledge": [36]}
  408. {"id": 48, "knowledge": "Multi-Phase Documentation Project", "description": "Defines complex archaeological projects requiring multiple scanning phases with integrated documentation strategy.", "definition": "A project with multiple scans where the total ADC < 70 for individual scans but DPQ > 80 when combined, where ADC is Archaeological Documentation Completeness and DPQ is Digital Preservation Quality, indicating comprehensive documentation achieved through multiple phases with coherent registration strategy for holistic interpretation.", "type": "domain_knowledge", "children_knowledge": [9, 38]}
  409. {"id": 49, "knowledge": "Equipment Optimization Opportunity", "description": "Identifies scenarios where equipment settings could be optimized for better results based on performance analysis.", "definition": "Scanning scenarios where EER < 30 but ESI > 80, where EER is Equipment Effectiveness Ratio and ESI is Environmental Suitability Index, indicating potential for improved equipment utilization in favorable conditions through calibration adjustment and scanning parameter optimization.", "type": "domain_knowledge", "children_knowledge": [39, 7]}
  410. {"id": 50, "knowledge": "Environmental Condition Classification System (ECCS)", "description": "A comprehensive classification system for archaeological site environments based on their suitability for scanning operations.", "definition": "A four-tier classification where 'Optimal Scanning Conditions' have ESI > 85, 'Good Scanning Conditions' have ESI between 70-85, 'Acceptable Scanning Conditions' have ESI between 50-70, and 'Challenging Scanning Conditions' have ESI < 50. This classification guides scanning schedule planning and equipment selection to maximize data quality.", "type": "domain_knowledge", "children_knowledge": [7, 15]}
  411. {"id": 51, "knowledge": "Workflow Efficiency Classification", "description": "A standardized categorization system for assessing processing workflow efficiency based on Processing Resource Utilization (PRU) values.", "definition": "A three-tier classification where 'Optimized' workflows have PRU < 5.0 (highly efficient resource usage), 'Acceptable' workflows have PRU between 5.0-10.0 (reasonable efficiency), and 'Needs Optimization' workflows have PRU > 10.0 (inefficient resource usage requiring intervention). This classification guides processing workflow improvements and resource allocation decisions.", "type": "domain_knowledge", "children_knowledge": [37]}
  412. {"id": 52, "knowledge": "Risk Zone Category", "description": "Classification system that evaluates archaeological sites for degradation risk based on preservation status and structural condition.", "definition": "Categorizes archaeological sites into two main groups: 'Degradation Risk Zone' and 'Not in Risk Zone'. 'Not in Risk Zone' means that the site is not in a Degradation Risk Zone.", "type": "domain_knowledge", "children_knowledge": [14, 26]}
  413. {"id": 53, "knowledge": "Mesh Quality Classification", "description": "A standardized system for categorizing archaeological site documentation based on the presence and quality of 3D mesh models.", "definition": "A three-tier classification where 'Has High-Fidelity Meshes' indicates sites with at least one mesh meeting high-fidelity criteria, 'Standard Mesh Quality' indicates sites with meshes that don't meet high-fidelity standards, and 'No Mesh Data' indicates sites lacking 3D mesh documentation entirely. This classification helps prioritize additional documentation efforts and determines appropriate analytical approaches for different sites.", "type": "domain_knowledge", "children_knowledge": [13]}
  414. </СЛОВАРЬ ЗНАНИЙ, СВЯЗАННЫЙ С БАЗОЙ ДАННЫХ>
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