MétaCan
Menu
← Back to cohort
Record W2004265977 · doi:10.1139/f2012-093

The bioenergetics of density-dependent growth in Arctic char (<i>Salvelinus alpinus</i>)

2012· article· en· W2004265977 on OpenAlexaffvenue
Guillaume Guénard, Daniel Boisclair, Ola Ugedal, Torbjørn Forseth, Ian Fleming, Bror Jönsson

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMemorial University of NewfoundlandUniversité de MontréalUniversité du Québec à Montréal
FundersInstitute of Materials Research and Engineering
KeywordsArctic charSalvelinusGrowth rateBioenergeticsCharEcologyBiologyZooplanktonEnergeticsDensity dependenceArcticPopulation densityEnvironmental scienceZoologyFish <Actinopterygii>FisheryChemistryTroutMathematicsPopulationGeometry

Abstract

fetched live from OpenAlex

We explored the mechanisms of density-dependent growth in Arctic char ( Salvelinus alpinus ) by comparing the energetics of growth, consumption, and activity obtained under three replicated density treatments in a large-scale enclosure (90 m2surface area) experiment. The enclosures permitted the entry of zooplankton and allowed char to feed on the bottom and at the surface of the lake. We found a negative (power) relationship between growth and density. Char consumption rate decreased linearly with increasing density. Growth efficiency was affected by fish density in a similar manner as growth rate. Finally, activity increased with fish density and was particularly high at high densities. Our findings illustrate the complexity of the relationships among consumption, activity, growth rates, and fish density and bring further evidence to the possible involvement of behavioural mechanisms in density-dependent processes, notably by modulating activity costs with density.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 head0.013
GPT teacher head0.200
Teacher spread0.187 · 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

Citations13
Published2012
Admission routes2
Has abstractyes

Explore more

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