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Record W2264216166 · doi:10.1139/cjfas-2015-0340

Growth rate and abundance of common fishes is negatively related to dissolved organic carbon concentration in lakes

2016· article· en· W2264216166 on OpenAlexaffvenueabout
Pierre-Olivier Benoît, Beatrix E. Beisner, Christopher T. Solomon

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversité du Québec à MontréalMcGill University
Fundersnot available
KeywordsPerchDissolved organic carbonTroutAbundance (ecology)SalvelinusFisheryProductivityEnvironmental scienceBiologyEcologyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Dissolved organic carbon (DOC) can limit food web productivity in lakes, potentially imposing resource limitation on fishes. We asked whether the abundance or early growth rate of three fish species was negatively related to DOC in 59 lakes in southern Quebec, Canada, where DOC concentrations ranged from 4 to 16 mg·L−1 for lakes containing walleye (Sander vitreum) and yellow perch (Perca flavescens) and from 2.6 to 9 mg·L−1 for lakes containing lake trout (Salvelinus namaycush). Estimates of abundance and growth rate were more precise for walleye and lake trout than for yellow perch because of differences in sample size. Abundance was negatively related to DOC for walleye and perhaps also for lake trout and yellow perch. Early growth rate was negatively related to DOC for walleye and lake trout, but not for yellow perch. These results support a growing body of literature suggesting that the productivity of fish populations may be negatively related to DOC concentrations in lakes.

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)0.001
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 score0.681
Threshold uncertainty score0.642

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.009
GPT teacher head0.197
Teacher spread0.189 · 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

Citations33
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic Sciences→Same topicFish Ecology and Management Studies→French-language works237,207→