Data mining for real mining: A robust algorithm for prospectivity mapping with uncertainties
Bibliographic record
Abstract
Mineral prospectivity mapping is an emerging application for machine learning algorithms which presents a series of practical difficulties. The goal is to learn the mapping function which can predict the existence or absence of economic mineralization from a compilation of geoscience datasets (ie: bedrock type, magnetic signature, geochemical response etc). The challenges include sparse, imbalanced labels (mineralization occurrences), varied label reliability, and a wide range in data quality and uncertainty. In order to address these issues an algorithm was developed based on total least squares and support vector machine regression which incorporates both data and label uncertainty into the objective function. This was done without losing sparsity in the residuals, thus maintaining minimal support vectors. Mineral prospectivity mapping is an application for machine learning which presents a series of practical difficulties. The goal is to learn the mapping function which can predict the existence of mineralization from a compilation of geoscience datasets. Challenges include sparse, imbalanced labels, varied label reliability, and a wide range in data uncertainty. To address this, an algorithm was developed based on TLS and SVM which incorporates both data and label uncertainty into the objective function.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".