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Record W4312077100 · doi:10.1093/icesjms/fsac215

Feeding by larval fish: how taxonomy, body length, mouth size, and behaviour contribute to differences among individuals and species from a coastal ecosystem

2022· article· en· W4312077100 on OpenAlexaffabout
Pierre Pepin

Bibliographic record

VenueICES Journal of Marine Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsPredationBiologyTaxonEcologyIchthyoplanktonLarvaEcosystemZoology

Abstract

fetched live from OpenAlex

Abstract Data on individual stomach contents were used to describe length-dependent differences in feeding success of larvae of 11 species of fish found in coastal Newfoundland, Canada. Copepods dominated the diet with a gradual shift from nauplii to copepodites in all species. Differences in feeding success in both prey number and gut fullness among individual larvae was linked to increasing individual diet diversity in all taxa, although there was a weak decline in mean prey size. Maxilla and body length, within and among taxa, have a dominant positive influence on the potential feeding success of larval fish. In addition to differences in average stomach weight, the variability in number of prey per stomach among individuals indicates that each species perceives their prey environment in different ways. Taxonomic proximity had limited effect on differences in feeding success among taxa. The results suggest that behavioural differences among individuals and taxa, that likely reflect swimming capacity and/or prey perception/capture ability, are likely to be important elements contributing to feeding success. Body and mouth size may represent key characteristics that should be considered in evaluating differences in feeding success among species as well as among individuals within and among cohorts.

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 score0.130
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.195
Teacher spread0.184 · 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

Citations35
Published2022
Admission routes2
Has abstractyes

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