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Record W1996413630 · doi:10.1139/cjfas-2012-0302

Hatchery tank enrichment affects cortisol levels and shelter-seeking in Atlantic salmon (<i>Salmo salar</i>)

2013· article· en· W1996413630 on OpenAlexaffvenue
Joacim Näslund, Malin Rosengren, Diego del Villar‐Guerra, Lars Christian Gansel, Johnny Norrgård, Lo Persson, John Winkowski, Eli Kvingedal

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsMemorial University of Newfoundland
FundersNorsk institutt for naturforskningSkogs- och Jordbrukets Forskningsråd
KeywordsSalmoHatcheryFisheryBiologyFish <Actinopterygii>Environmental science

Abstract

fetched live from OpenAlex

Stocking programs using hatchery-reared salmon are often implemented for augmenting natural populations. However, survival of these fish is often low compared with wild conspecifics, possibly because of genetic, physiological, and behavioural deficiencies. Here, we compared presmolt Atlantic salmon (Salmo salar) from three different environmental treatments (barren environment, plastic tube enrichment, and plastic shredding enrichment) with regard to plasma cortisol levels, shelter-seeking behaviour, and fin deterioration. Basal plasma cortisol levels were higher in barren-reared fish, indicating higher stress levels, while no differences were found in acute cortisol response after a 30 min confinement test. Shelter-seeking was higher in salmon reared in enriched tanks when tested alone, but not when tested in small groups. Barren-reared fish had higher levels of fin deterioration over winter, potentially owing to higher aggression levels. These results suggest that enrichment can reduce the impact of stressors experienced in the hatchery and thus increase fish welfare. Tank enrichment may also be used to produce salmon better adapted for the more complex environment encountered after release.

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)0.000
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 score0.124
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.207
Teacher spread0.192 · 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

Citations141
Published2013
Admission routes2
Has abstractyes

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