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Record W2061159493 · doi:10.1139/f06-182

Nonrandom mating in a broadcast spawner: mate size influences reproductive success in Atlantic cod (<i>Gadus morhua</i>)

2007· article· en· W2061159493 on OpenAlexvenueno aff
Sherrylynn Rowe, Jeffrey A. Hutchings, Jon Egil Skjæraasen

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGadusBiologyAtlantic codGadidaeReproductive successMatingZoologyMating systemEcologyPopulationSexual selectionSperm competitionMate choiceFisheryDemographyFish <Actinopterygii>

Abstract

fetched live from OpenAlex

We tested the hypothesis that reproductive success in Atlantic cod (Gadus morhua) is random with respect to similarity in body size between mates. Immediately prior to their natural breeding periods, groups of 52–93 cod from three Northwest Atlantic populations were transported to a large (15 m diameter, 4 m deep) tank where they spawned undisturbed at densities similar to those in nature. Based on microsatellite DNA-parentage assignment of 8913 offspring from four spawning groups, females and males achieved their highest reproductive success when breeding with mates that were larger than themselves. Our observations are consistent with the hypothesis that some form of intrasexual competition or mate choice is a constituent of the mating system of this species and that this can have an important influence on individual fitness. Our results further suggest that reductions in the mean and variance in body size of commercially exploited marine fishes concomitant with size-selective harvesting may have greater negative consequences for population recovery than previously thought.

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)0.002
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.222
Teacher spread0.211 · 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

Citations34
Published2007
Admission routes1
Has abstractyes

Explore more

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