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Record W3113618030 · doi:10.1139/cjfas-2020-0236

Growth during the first summer at sea modulates sex-specific maturation schedule in Atlantic salmon

2020· article· en· W3113618030 on OpenAlexvenueno aff
Cécile Tréhin, Étienne Rivot, Ludivine Lamireau, Lisa Meslier, Anne‐Laure Besnard, Stephen D. Gregory, Marie Nevoux

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersInterregEuropean Regional Development Fund
KeywordsSalmoBiologyPopulationSexual maturityEcologyFisheryZoologyGeographyFish <Actinopterygii>Demography

Abstract

fetched live from OpenAlex

Recent decline in abundance of Atlantic salmon (Salmo salar) and concomitant changes in life history may result from a decline in the growth conditions during marine migration. Available literature suggests the existence of a sex-specific reaction norm linking maturation with environmental growth conditions at sea. However, the extent to which this mechanism explains variations in age at maturity remains unclear. Using a historical collection of scales (1987–2017) from the Sélune River, France, we showed that marine growth declined over the first summer and remained stable during the subsequent periods at sea among returning salmon. Results support the hypothesis of a sex-specific probabilistic reaction norm, with individual probability to return after 1 year at sea increasing when growth increases. Females may require higher growth than males to attain their maturation threshold. This mechanism is a good candidate to explain temporal variability in sea-age at return at both the individual and population level in the Sélune population and in many other southern European populations.

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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.016
GPT teacher head0.186
Teacher spread0.170 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

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