Recursive Annotation for Negative Labeling in Data-Driven Mineral Prospectivity Mapping
Bibliographic record
Abstract
Abstract Data engineering is a challenge in mineral prospectivity mapping using supervised data-driven methods because of a general paucity of true samples, both positive and negative. Naïve random sampling of unlabeled samples as presumptive negative samples is a common method, but it creates label crossover, which is the condition that otherwise true positives would be reversed in class designation. For mineral prospectivity mapping, label crossover has two main effects: (1) it artificially decreases the area of positive prospectivity because label crossover is asymmetrical; and (2) it increases model variance by increasing model complexity and, therefore, weakens the spatial continuity of predictions. These effects reduce the realism and trustworthiness of spatial analytic products (e.g., maps), which hinders their adoption. Here, we propose a data-driven and recursive annotation method based on positive and unlabeled learning to annotate negative samples. Our method progressively biases the negative labeling of unlabeled samples using prospectivity scores (as a class prior). The optimization of the number of recursions occurs through a joint maximization of the variable (aspatial) and spatial domain objectives of prospectivity mapping. Reducing label crossover improves the performance of a deep ensemble (of unique workflows) and increases spatial continuity of the resulting prospectivity map. Only two iterations of annotation were necessary for the refinement of a graphite prospectivity map in Canada. By its design, recursive annotation is broadly compatible with a range of workflows.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".