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Record W2149846478 · doi:10.1111/eff.12148

Can conservation‐oriented, captive breeding limit behavioural and growth divergence between offspring of wild and captive origin <scp>A</scp>tlantic salmon (<i><scp>S</scp>almo salar</i>)?

2014· article· en· W2149846478 on OpenAlexafffund
Nathan F. Wilke, Patrick O’Reilly, Danielle A. Macdonald, Ian Fleming

Bibliographic record

VenueEcology Of Freshwater Fish · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsHatch (Canada)Bedford Institute of OceanographyMemorial University of Newfoundland
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaFonds en Fiducie pour la Faune du Nouveau-BrunswickMountain Equipment Co-operative
KeywordsDomesticationCaptivityCaptive breedingBiologyThreatened speciesEndangered speciesPopulationSalmoEcologyZoologyFisheryDemographyFish <Actinopterygii>Habitat

Abstract

fetched live from OpenAlex

Abstract Captive rearing is being used increasingly to maintain demographics and genetic diversity of threatened fish populations and species, but its effectiveness can be hindered by domestication, that is, inadvertent selection for performance in captivity at the cost of that in the wild. Some captive rearing programmes have begun to take steps to limit such domestication, but the results are ambiguous, as the degree of generational exposure to captivity is often difficult to control. Using an endangered population of Atlantic salmon ( Salmo salar ) currently undergoing conservation‐oriented captive rearing, we tested for domestication effects on dominance (dyadic trials) and growth (seminatural stream channels with differing densities and group proportions) of juvenile offspring of wild and captive origin parents. Pedigree data afforded the ability to compare these effects among three specific study groups: wild, single‐generation captives and two to three generation captives. Our results indicate that, despite conservation breeding practices, a divergence in growth can occur in as little as one generation without divergence in dominance behaviour. Further, evidence suggests that trait divergence did not increase with generations in captivity. Given the experimental design, results and supporting literature, we conclude that this contemporary divergence is likely genetic and driven by a combination of factors, including variation in selective histories influencing behaviour.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.015
GPT teacher head0.204
Teacher spread0.189 · 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.

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

Citations19
Published2014
Admission routes2
Has abstractyes

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