An Exploration of False Positives Following Multispectral Analysis of Mine Tailings Extent
Bibliographic record
Abstract
Satellite imagery can be analyzed to offer a preliminary regional assessment of mine tailings indicators, enabling identification before performing in-depth fieldwork. Nova Scotia, Canada, still retains mine tailings produced in the 1860s to the 1940s in 64 historic gold districts, which exceed soil guidelines for arsenic (As) and mercury (Hg) levels. Tailings data often relies on historical maps, which may not accurately depict the current extent due to wind and rain transportation. A classification model was designed to analyze multispectral Sentinel-2 images and indicate pixels potentially comprising tailings. This classifier had an overall F1-Score about 0.7 for most methods tested, but this accuracy was not consistent across all land cover types. In particular, wetland and coastal areas seemed to generate a high number of false positives. In this study, these potential false positives are investigated to better understand the model’s confusion and improve future results.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".