MétaCan
Menu
Back to cohort
Record W1993150679 · doi:10.1139/t08-132

Oxygen diffusion and consumption in low-sulphide tailings covers

2009· article· en· W1993150679 on OpenAlexafffundvenueabout
Isabelle Demers, Bruno Bussière, Mamert Mbonimpa, Mostafa Benzaazoua

Bibliographic record

VenueCanadian Geotechnical Journal · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
FundersNatural Sciences and Engineering Research Council of CanadaIAMGOLD
KeywordsTailingsOxygenMoistureEnvironmental scienceWater tableDiffusionLimiting oxygen concentrationEnvironmental engineeringGeologyChemistryGeotechnical engineeringMetallurgyMaterials scienceGroundwater

Abstract

fetched live from OpenAlex

Acid-generating tailings can be reclaimed using oxygen barrier covers in humid climates, such as in Quebec. A single-layer low-sulphide tailings cover is an attractive alternative to traditional water covers to maximize the impoundment’s storage capacity. Low-sulphide tailings can serve as a moisture-retaining material to limit oxygen diffusion, and their residual sulphides can consume the diffusive oxygen that still gets through the cover. A laboratory experiment using instrumented columns was initiated to confirm the efficiency of a low-sulphide tailings monolayer cover placed over acid-generating tailings to reduce the oxygen flux reaching the reactive tailings. Oxygen concentration profiles were measured over the thickness of the cover and oxygen fluxes were calculated to evaluate the effect of three parameters on these fluxes: water table level, cover sulphide content, and cover thickness. Oxygen fluxes at the bottom of the low-sulphide tailings cover layer, calculated using oxygen gradient and estimated effective diffusion coefficient and predicted using Vadose/W, were lower than 1.5 mol·m −2 ·year −1 . These results suggest that a low-sulphide tailings cover with an elevated water table can reduce oxygen migration and potentially limit acid mine drainage generation.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.885

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)0.0010.000

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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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 designObservational
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

Citations47
Published2009
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicMine drainage and remediation techniquesFrench-language works237,207