Integrated Multi-Parameter Exploration Footprints of the Canadian Malartic Disseminated Au, McArthur River-Millennium Unconformity U, and Highland Valley Porphyry Cu Deposits: Preliminary Results from the NSERC-CMIC Mineral Exploration Footprints Research Network
Bibliographic record
Abstract
Mineral exploration in Canada is increasingly focused on concealed and deeply buried targets, requiring more effective tools to detect large-scale ore-forming systems and to vector from their most distal margins to their high grade cores. A new generation of ore system models is required to achieve this. The Mineral Exploration Footprints Research Network is a consortium of 70 faculty, research associates, and students from 20 Canadian universities working with 30 mining, mineral exploration, and mining service providers to develop new approaches to ore system modelling based on more effective integration and visualization of multi-parameter geological-structural-mineralogical-lithogeochemical-petrophysical-geophysical exploration data. The Network is developing the next generation ore system models and exploration strategies at three sites based on integrated data visualization using self-consistent 3D Common Earth Models and geostatistical/machine learning technologies. Thus far over 60 footprint components and vectors have been identified at the Canadian Malartic stockwork-disseminated Au deposit, 20–30 at the McArthur-Millennium unconformity U deposits, and over 20 in the Highland Valley porphyry Cu system. For the first time, these are being assembled into comprehensive models that will serve as landmark case studies for data integration and analysis in the today’s challenging exploration environment.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".