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Record W4416663800 · doi:10.1080/17480930.2025.2586063

Influence of climate and deposition parameters on acid mine drainage risks from filtered tailings facilities

2025· article· en· W4416663800 on OpenAlexafffund
Corentin Chevalier, Madison Chai-Onn, Isabelle Demers

Bibliographic record

VenueInternational Journal of Mining Reclamation and Environment · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsUniversité du Québec en Abitibi-TémiscaminguePolytechnique MontréalNatural Sciences and Engineering Research Council of Canada
FundersFonds de recherche du Québec – Nature et technologies
KeywordsTailingsDeposition (geology)Acid mine drainageDrainageTailings dam

Abstract

fetched live from OpenAlex

Filtered tailings stockpiles help reduce geotechnical risks due to minimised water management, but at the same time, prolonged exposure to atmospheric conditions may increase the risk of acid mine drainage (AMD) generation. Unsaturated and porous media flow simulations, calibrated using field data and extrapolated, were conducted to assess variations in the degree of saturation and oxygen flux with depth. Results indicated that exposed filtered tailings rarely reached a degree of saturation above 90% to prevent AMD. However, strategic tailings deposition planning can limit AMD generation risk (i.e. maintaining a near neutral pH) by limiting oxygen flux to the lower layers as deposition progresses.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.251
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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