Methodology for portraying 3D structure using ArcGIS: a test case from the southern Canadian Rocky Mountains, British Columbia and Alberta
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the study of structural geology, a three-dimensional (3D) geologic cross-section plays an important role in the understanding of subsurface structures and their geometric relationships. This Open File report describes the procedural workflow followed to construct 3D cross-sections entirely within the ESRI ArcGIS software suit. The ArcGIS components involved include ArcMap, ArcScene and ArcCatalog (version 10.5.1), and extensions comprising 3D Analyst, Spatial Analyst and a third-party ArcMap plugin called Xacto Section Tools that was developed by the Illinois State Geological Survey. ArcGIS allows the processing and analysis of vector (e.g. geological surface, faults, cross-section lines, etc.) and raster data (digital elevation model (DEM), surface) to create 3D cross-sections and fence diagrams with a high degree of spatial accuracy. This method utilized surface information from digital bedrock geological maps, 2D structural cross-sections and a DEM derived from the geological map contours. Shapefiles of 3D cross-sections, the bedrock geological map, style file for cross-sections and geological map, DEM, cross-section lines, and fault data are included in this report for visualization in ArcGIS software. Three movie files (.avi) are included for viewing without ArcGIS software. The methodology successfully allowed the 3D viewing of the structural geometry of the study area and should be applicable to for other geographic locations and geologic settings. Contact surfaces consistent with the map and cross-section data were readily created using ArcGIS in areas with minimal faulting. However, in areas with structural overlap caused by reverse faulting significant segmentation of the input data was required to generate meaningful surfaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it