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Record W2775592800 · doi:10.1080/20442041.2017.1401702

Intensity and duration of effects of impoundment on mercury levels in fishes of hydroelectric reservoirs in northern Québec (Canada)

2017· article· en· W2775592800 on OpenAlexafffundabout
François Bilodeau, Jean Therrien, Roger Schetagne

Bibliographic record

VenueInland Waters · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsHydro-Québec
FundersHydro-QuébecCisco Systems
KeywordsCatostomusEsoxCoregonus clupeaformisPikeSalvelinusSuckerFisheryTroutEnvironmental scienceEcologyBiologyHydrology (agriculture)Fish <Actinopterygii>GeologyZoology

Abstract

fetched live from OpenAlex

At the La Grande Hydroelectric Complex (Québec, Canada), total mercury (THg) levels in fish were monitored from 1978 to 2012 in more than 37 000 fish comprising 5 species: lake whitefish (Coregonus clupeaformis), longnose sucker (Catostomus catostomus), northern pike (Esox lucius), walleye (Sander vitreus), and lake trout (Salvelinus namaycush). In reservoirs, concentrations of all species increased rapidly after impoundment, peaking after 4–11 yr in nonpiscivorous species and after 9–14 yr in piscivorous species, at levels 2–8 times higher than those measured in surrounding natural lakes. In fish of standardized length, maximum levels reached 0.33–0.72 μg g−1 in nonpiscivorous species and 1.65–4.66 μg g−1 in piscivorous species. Depending on the reservoir, the return to levels equivalent (p < 0.05) to those found in fish in surrounding natural lakes was completed after 10–20 yr for all nonpiscivorous species and after 20– 31 yr in most piscivorous species, if no additional flooding occurred. These results tend to confirm the findings of other authors suggesting that the following reservoir characteristics play a major role in determining the intensity and duration of after-impoundment THg increases in fish: flooded area, annual volume of water flowing through the reservoir, filling period, water temperature, and percentage of flooded area located in the drawdown zone.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.434

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 head0.226
Teacher spread0.213 · 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

Citations32
Published2017
Admission routes3
Has abstractyes

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