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Record W2946582906

Bioaccumulation and transfer of mercury and arsenic in aquatic invertebrates and emergent insects at historical gold mine tailing sites of Nova Scotia

2019· article· en· W2946582906 on OpenAlexfundaboutno aff
Molly E. LeBlanc

Bibliographic record

VenueSaint Mary's University Institutional Repository (Saint Mary's University) · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change Canada
KeywordsBioaccumulationNova scotiaMercury (programming language)InvertebrateEnvironmental scienceTailingsGold miningPlacer miningEnvironmental chemistryMining engineeringEcologyGeologyOceanographyChemistryBiologyGeochemistry
DOInot available

Abstract

fetched live from OpenAlex

Historical gold mining in Nova Scotia resulted in over 3,000,000 tonnes of mine tailings deposited into aquatic habitats and low-lying areas, where they remain today.Legacy tailings are typically elevated in mercury (Hg) and arsenic (As), however, aquatic ecological effects remain largely unquantified to date.An initial literature review revealed only three studies mentioning contaminants in aquatic invertebrates at tailing sites.My objective was to assess [Hg] and [As] in aquatic invertebrates living on tailings-affected wetlands, and the role of emergent insects as biovectors of these contaminants.Samples showed that sediment and water at tailings sites were elevated in Hg and As, often surpassing CCME guidelines.Aquatic invertebrates from tailings sites had elevated [Hg] (up to 4.20 ppm).Invertebrate [As] frequently exceeded CCME guidelines for fish.Adult emergent insects were shown to be likely biovectors of Hg, while As was largely shed with casings during hatching.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.085
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.014
GPT teacher head0.191
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2019
Admission routes2
Has abstractno

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