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Record W2038654347 · doi:10.1139/cjfas-2012-0247

Fin erosion and injuries in relation to adult recapture rates in cultured smolts of Atlantic salmon and brown trout

2013· article· en· W2038654347 on OpenAlexvenueno aff
Erik Petersson, Lars Karlsson, Bjarne Ragnarsson, Marcus Bryntesson, Anders Berglund, Stefan Stridsman, Sara Jönsson

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersHavs- och Vattenmyndigheten
KeywordsSalmoBrown troutHatcheryStockingDorsal finFisheryTroutBiologyFinFish <Actinopterygii>Animal science

Abstract

fetched live from OpenAlex

The implications of fin erosion and other injuries that are directly or indirectly caused by the hatchery environment have long been debated. Fin condition has been regarded as an indicator of welfare in fish farms, but until now there has been little evidence that eroded fins have negative effects on survival after stocking in the wild. Based on over 40 years of tagging and recapture data, we show that Atlantic salmon (Salmo salar) with dorsal fin erosion and brown trout (Salmo trutta) with any kind of injuries had lower recapture rates than fish without injuries. In salmon, precocious mature males had a lower probability of being recaptured compared with immature fish. Data from a hatchery monitoring program indicated that the degree of fin erosion on the dorsal fin in salmon and on the caudal fin in trout was correlated with the number of other injuries. We conclude that fin erosion and other injuries may reduce the probability of survival after release. All actions in the hatcheries to reduce fin erosion and other injuries will most likely be positive for the long-term outcome of the stocking programs.

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 score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.009
GPT teacher head0.201
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 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

Citations17
Published2013
Admission routes1
Has abstractyes

Explore more

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