Bibliographic record
Abstract
Assigning data to a 3D framework is important for 3D modelling as it provides the backbone for all analysis. Linear Referencing is presented here as a method to capture and register drill hole data in a digital 3D work environment. Several specialized 3D modelling applications currently exist that provide similar workflows however, these applications may not be accessable due to high costs and limited user bases. Additionally, a large portion of the geoscience community regularly uses full featured 2D GIS platforms such as ESRI© ArcmapTM and in 3D with 3D AnalystTM and ArcSceneTMto store and analyse their data. Currently, performing linear referencing in ArcMapTM and ArcSceneTM is a complicated process with limited documentation . This open file was created to guide users through the workflow from data compilation to a 3D georeferenced dataset. The workflow is demonstrated using real world data from the Sullivan Mine, Kimberly, British Columbia and a test dataset.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.019 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".