Knowledge-driven mineral prospectivity modelling in areas with glacial overburden: porphyry Cu exploration in Quesnellia, British Columbia, Canada
Bibliographic record
Abstract
Modern mineral exploration involves making discoveries in geological environments where detecting deposits is increasingly difficult. This study presents an integrated, knowledge-driven prospectivity mapping approach to exploration in the glacial till-covered region of the Quesnel Terrane in British Columbia, Canada. The Quesnel Terrane hosts base and precious metal deposits; however, a ∼20,000 km2 area consisting of variably thick glacial overburden truncates surface exposure, likely masking buried deposits. Through a combined fuzzy logic and index overlay approach, geophysical, structural, and geochemical criteria were integrated into a prospectivity model. Three concealed porphyry Cu targets were identified in the till-covered region and were validated by statistical analysis of lake and stream sediment geochemistry. The process of integrating various geoscientific disciplines and identification of targets has provided valuable insight for future exploration in covered regions. Moreover, the adopted approach leverages open-source public datasets, highlighting advantages of utilising such datasets for regional-scale exploration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".