3-D Integrated Geological Modeling in the Abitibi Subprovince (Quebec, Canada): Techniques and Applications
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
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
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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