3-D Integrated Geological Modeling in the Abitibi Subprovince (Quebec, Canada): Techniques and Applications
Bibliographic record
Abstract
Abstract — The development of robust 3-D geological models involves the integration of large amounts of public geological data, as well as additional accessible proprietary lithological, structural, geochemical, geophysical, and diamond drill hole data. 3-D models and maps have been available, particularly in the petroleum industry, for more than 10 years. Here, we demonstrate how robust 3-D maps can be used as interactive tools for mineral deposits exploration. In particular, we show how the interrogation of 3-D data sets can constrain exploration targets at depth. The main advantages of this technique for the mining industry are the homogeneity of data treatment and the validation of geological interpretations, taking into account geophysical and geochemical data. Data integration and cross-correlation of geology and geophysics can be achieved in two dimensions in any good GIS package. However, the added strength of 3-D analysis is the integration of separate data sets in three dimensions to build more complete, more realistic models, and in delineating areas of high economic potential at depth. Furthermore, these models can be modified and improved at any time by adding new data from ongoing drilling and geoscientific surveys. This paper presents two examples of 3-D models used for mineral exploration: the Joutel VMS
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".