Beyond ML/ARD : the many faces of neutral mine drainage in the context of mine closure
Bibliographic record
Abstract
The ability to predict and manage the interactions between mine wastes and water, with the associated implications for aquatic ecosystem impairment, has evolved immensely over the last two decades and continues to rapidly change. The major focus so far has been on drainage chemistry from sulphidic ores and wastes, since oxidation of pyrite, pyrrhotite and similar sulphide minerals has been the root cause of serious water-quality issues at many mine sites worldwide. However, there are also many examples of compromised surface water and groundwater quality as a result of geochemical processes involving neutral to alkaline dissolution and aqueous transport. Neutral mine drainage (NMD) is often taken to mean down-gradient runoff from sulphidic source materials that are undergoing oxidation following the subsequent reaction with neutralising minerals such as carbonates; this results in water with circumneutral pH, high hardness and high sulphate levels. There is no standardised definition of NMD, and this term is increasingly used by researchers and managers to describe a variety of geochemical processes and issues. The predictive models and tools for managing the environmental effects of NMD are more poorly developed than for acidic rock drainage (ARD), especially since predictions depend more on complex interpretations of mineralogy and geochemistry. The flux of various trace major elements and materials from mine spoils to the hydrosphere is generally far greater for ARD than NMD; however, the potential for ecosystem-scale impacts from NMD is expected to increase in proportion with an increase in the spatial scale of mining projects in general. This paper provides a simplified classification of the various types of NMD that have been encountered as a starting point for developing new predictive and management approaches for NMD. Several of the tools for preventing NMD-related environmental impacts, applied during mine development and closure planning, are very similar to the tools that have been developed for ARD; however, the tools and approaches for NMD place greater emphasis on management actions at the watershed scale and on interactions between groundwater and surface water.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".