Orogenic gold mineral prospectivity mapping of the geraldton area, ontario: discussion of key issues
Bibliographic record
Abstract
This paper employed Random Forests (RF) to generate several Mineral Prospectivity maps for orogenic gold in the Geraldton area, located within the Wabigoon Tectonic subprovince of Ontario, Canada. Various issues pertinent to the Mineral Prospectivity mapping process are presented and proposed solutions to these key challenges are suggested. Additionally, multiple methods are proposed to analyze text-based geoscientific information derived from geological maps, including a novel application of Natural Language Processing (NLP) to delineate the sources and traps of gold mineral systems. The Mineral Prospectivity maps generated have located new possible areas for gold exploration. Concerning the key issues addressed in the paper, (1) the results from NLP have contributed to significant predictor maps for gold exploration, (2) the method for creating a non-deposit class for input to the random forests machine learning algorithm was found to involve creating points at least 2 km from existing Au deposits\occurrences, (3) a weighting method for existing Au deposits based on tonnage produced was successfully introduced and (4) methods of producing ensemble combinations of the Mineral Prospectivity maps were produced and discussed. The results produced from the paper should significantly enhance Au exploration in the Geraldton area of Ontario, Canada.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".