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Record W4241290865 · doi:10.1093/plankt/fbn030

Effects of intra- and inter-annual variability in prey field on the feeding selectivity of larval Atlantic mackerel (Scomber scombrus)

2008· article· en· W4241290865 on OpenAlexaff
Dominique Robert, Martín Castonguay, Louis Fortier

Bibliographic record

VenueJournal of Plankton Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPredationBiologyCopepodScomberZooplanktonLarvaMackerelIchthyoplanktonFisheryScombridaeEcologyZoologyCrustaceanTunaFish <Actinopterygii>

Abstract

fetched live from OpenAlex

We identified to the lowest taxonomical level possible the preferred prey of Atlantic mackerel larvae from the southern Gulf of St Lawrence and assessed the extent to which prey selectivity varied within and among years. Mackerel larvae and their zooplankton prey were sampled in the summer of four consecutive years (1997–2000). The nauplii of the calanoid copepod Pseudocalanus sp. strongly dominated the diet of larvae <7 mm both in terms of numbers and carbon content, whereas larvae ≥7 mm mainly fed on fish larvae (including conspecifics) and cladocerans. Chesson's alpha index revealed strong selectivity in all years for Pseudocalanus sp. nauplii in first-feeding larvae. Selectivity shifted to cladocerans and fish larvae around a body length of 7 mm. Intra- and inter-annual prey selectivity changes were mainly observed for alternative prey, during the period surrounding the shift in diet from small to large prey. Our results underscore the importance of considering the availability of the main prey Pseudocalanus sp. nauplii (early larval stage) as well as cladocerans and fish larvae (late larval stage), rather than the entire prey field in the future assessment of the role played by prey availability on larval mackerel vital rates.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.023
GPT teacher head0.293
Teacher spread0.269 · 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

Citations54
Published2008
Admission routes1
Has abstractyes

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