Interactions des résidus miniers et du lixiviat d'une couverture de sous-produits de desencrage dans le contexte du controle du drainage minier acide
Bibliographic record
Abstract
Mine-wastes contain sulfide minerals, which are able to react with atmospheric water and oxygen to produce sulfuric acid. This production induces the decrease of pH and favors the solubilization of heavy metals. These reactions are called acid mine drainage and is one of the environmental priority of mine industry. To prevent the oxygen flow through the mine tailings, a cover of deinking by-products were set on two mine sites: Eustis and Albert (PQ, Canada). The production of an organic-rich leachate from the cellulose degradation was noticed. This leachate eventually flows through the mine tailings. This study aims at understanding the interactions between the organic leachate and mine tailings in the point of view of preventing acid mine drainage and of the biodegradation of the organic components from the leachate. In-situ observations, batch and column essays showed that the anaerobic environment induced by the cover of deinking by-products, the seepage and the organic- and carbonates-rich leachate cause an increase of the pH and improve the characteristics of the oxidized mine tailings. The initiation of acid mine drainage was not noticed in the unoxidized and mixed tailings. Moreover, sulfate-reduction and methanogenesis allowed the attenuation of the organic components from the deinking by-products leachate, producing alkalinity that improved the acid mine drainage characteristics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".