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Record W2106524286 · doi:10.1139/f05-210

The logic of skipped spawning in fish

2006· article· en· W2106524286 on OpenAlexvenueno aff
Christian Jørgensen, Bruno Ernande, Øyvind Fiksen, Ulf Dieckmann

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsGadusFishingGadidaeFisheryBiologyAtlantic codStock (firearms)Semelparity and iteroparityReproductive successReproductionEcologyFish <Actinopterygii>GeographyPopulationDemography

Abstract

fetched live from OpenAlex

That sexually mature fish skip reproduction, especially in response to poor condition, has been documented in many species. We present results from an energy-allocation life history model that shed light on the underlying logic of skipped spawning, based on the Northeast Arctic stock of Atlantic cod (Gadus morhua). The model predicts that skipped spawning is a regular phenomenon, with up to 30% of the sexually mature biomass skipping spawning. Spawning should be skipped if the expected future gain in reproductive output, discounted by survival, more than balances the expected reproductive success the current year. Skipped spawning was most common (i) among potential second-time spawners and (ii) early in life, (iii) when fishing mortality at the spawning grounds was high, (iv) when fishing mortality at the feeding grounds was low, (v) when natural mortality was low, and (vi) when the energetic and mortality costs associated with migration and spawning were high. Cod skipped spawning more often when food availability was both increased (opportunities for better growth) and decreased (too little energy for gonad development), and this pattern interacted with mortality rate. We conclude that skipped spawning may be more widespread than appreciated and highlight potential consequences for the understanding of stock–recruitment relationships.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.274
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.226
Teacher spread0.208 · 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 teacher head, 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

Citations282
Published2006
Admission routes1
Has abstractyes

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