Class Label Representativeness in Machine Learning-Based Mineral Prospectivity Mapping
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Mineral prospectivity mapping (MPM) can be deemed a binary classification task, with classifiers trained and validated on labels indicating the presence or absence of the targeted mineralized zones. Using economically viable mineral deposits as positive labels could, in theory, yield prospectivity models with geometallurgical reliability, thereby aiding land management and decision-making. The inherent scarcity of economically viable deposits, however, ultimately affects MPM products. The positive class label, therefore, often requires augmentation with either mineral occurrences (i.e., mineralized sites lacking economic viability) or synthetically generated labels. This paper examines how augmented positive labels and different negative label selection procedures geospatially represent economically viable mineral deposits and affect deep learning-based MPM’s classification performance and its spatial selectivity (i.e., MPM’s capability to efficiently narrow the exploration search space). To achieve this objective, large ensembles of deep learning classifiers were trained and validated with diverse combinations of positive and negative labels. Two positive class label sets were created by augmenting mineral deposits with either synthetic labels, generated using generative adversarial networks, or mineral occurrences, paired with distinct negative label sets selected based on (1) locations distant from known mineral deposits, (2) areas geospatially dissimilar to known mineral deposits, and (3) mineralized areas unrelated to the targeted style of mineralization, resulting in six unique class configurations. This study ultimately provides insights into how different label sets affect MPM's classification performance and spatial selectivity. The results indicate that selecting negative class labels from geospatially different localities enhances classification performance and MPM's spatial selectivity compared to other negative label selection procedures.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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