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Record W2514478017 · doi:10.1139/cjfas-2015-0515

Born to be wild: effects of rearing density and environmental enrichment on stress, welfare, and smolt migration in hatchery-reared Atlantic salmon

2016· article· en· W2514478017 on OpenAlexvenueno aff
Malin Rosengren, Eli Kvingedal, Joacim Näslund, Jörgen I. Johnsson, Kristina Sundell

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersVetenskapsrådetSvenska Forskningsrådet Formas
KeywordsHatcherySalmoStockingBiologyFisheryAnimal scienceSmoltificationEcologyFish <Actinopterygii>Salmonidae

Abstract

fetched live from OpenAlex

Hatchery-reared salmonids released into the wild generally have poor survivability compared with wild conspecifics. To assess potential hatchery rearing improvements, behavioral and physiological effects of reducing animal density and adding in-tank shelter were investigated. Atlantic salmon (Salmo salar) parr were placed in barren or shelter-enriched tanks at high or low density up until release as smolts. Lowered density rendered positive effects on growth and intestinal barrier function, and both lowered density and shelter decreased conspecific aggression, as inferred by fin damage. Furthermore, while the presence of shelter decreased stress hormone levels following human disturbance, it also decreased growth and smolt migration success, an effect particularly pronounced at high densities. Therefore, we suggest that this type of structural enrichment should be avoided for Atlantic salmon smolts held at high densities and conclude that a lowered animal density with or without shelter has the highest potential in producing a more resilient smolt for stocking.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

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 head0.007
GPT teacher head0.183
Teacher spread0.176 · 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 designBench or experimental
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

Citations87
Published2016
Admission routes1
Has abstractyes

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