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Record W2134011247 · doi:10.1002/2015gl063345

The impact of a catastrophic mine tailings impoundment spill into one of North America's largest fjord lakes: Quesnel Lake, British Columbia, Canada

2015· article· en· W2134011247 on OpenAlexafffundabout
Ellen L. Petticrew, Sam Albers, Susan A. Baldwin, Eddy C. Carmack, Stephen J. Déry, Nikolaus Gantner, Kelly E. Graves, B. Laval, J. Morrison, Philip N. Owens, Daniel T. Selbie, Svein Vagle

Bibliographic record

VenueGeophysical Research Letters · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsFisheries and Oceans CanadaUniversity of British ColumbiaUniversity of Northern British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Northern British Columbia
KeywordsHypolimnionFjordSeicheThermoclineGeologyTailingsOceanographyHydrology (agriculture)Turbidity currentStructural basinTurbidityEnvironmental scienceGeomorphologyEutrophication

Abstract

fetched live from OpenAlex

Abstract On 4 August 2014, a catastrophic breach of the Mount Polley mine tailings impoundment released ~25 M m 3 of tailings and water and scoured an unknown quantity of overburden into the West Basin of Quesnel Lake. We document Quesnel Lake and Quesnel River observations for 2 months postspill. Breach inflows raised Quesnel Lake by 7.7 cm, equivalent to ~21 M m 3 . The West Basin hypolimnion was modified immediately, exhibiting increased temperature (~5°C to 6–7.5°C), conductivity (110 to 160 μS/cm), and turbidity (<1 to 200–1000 nephelometric turbidity units (NTU)). Cooscillating seiches moved West Basin hypolimnetic water both westward and eastward contaminating the Main Basin. Postspill, high‐turbidity water propagated eastward (~1 cm/s), introducing a persistent ~20 m thick layer below the thermocline and an ~30 m thick layer at the bottom. The contaminant introduction, mobilization, and bioaccumulation may pose risks to resident and anadromous fish stocks, which support recreational, commercial, and First Nations fisheries.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.026
GPT teacher head0.290
Teacher spread0.264 · 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

Citations78
Published2015
Admission routes3
Has abstractyes

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