MétaCan
Menu
← Back to cohort
Record W2916551598 · doi:10.1038/s41598-019-39153-x

Cortisol predicts migration timing and success in both Atlantic salmon and sea trout kelts

2019· article· en· W2916551598 on OpenAlexafffund
Kim Birnie‐Gauvin, Hugo Flávio, Martin Kristensen, Sarah E. Walton‐Rabideau, Steven J. Cooke, William G. Willmore, Anders Koed, Kim Aarestrup

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsInnovationsfonden
KeywordsTroutFisheryFish <Actinopterygii>BiologyOceanographyEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

Kelts - individuals of anadromous fish species which have successfully spawned and may return to sea to repeat the cycle - are perhaps the least studied life stage of iteroparous fish species. To date, our understanding of what makes them successful in their return migration to sea is limited. We investigated the relationship between three physiological parameters (baseline cortisol, baseline glucose and low molecular weight antioxidants) and the timing and success of Atlantic salmon (Salmo salar) and sea trout (Salmo trutta) kelt migration. To do so, we combined blood samples obtained within 3 minutes of capture and acoustic telemetry to track 66 salmon and 72 sea trout as they migrated out of rivers, into fjords and out at sea. We show that baseline cortisol may be a good predictor of migration success. Individuals with high baseline cortisol levels exited the river earlier but were less likely to successfully reach the sea. Similar relationships were not observed with glucose or antioxidants. We provide the first evidence to support the role of physiological status in migration success in Atlantic salmon and sea trout kelts. Our findings contribute to our understanding of the relationship between physiology and fitness in wild animals. Further, we suggest that migration timing is a trade-off between stress and readiness to migrate.

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 score0.010
Threshold uncertainty score0.019

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 head0.211
Teacher spread0.204 · 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

Citations43
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueScientific Reports→Same topicFish Ecology and Management Studies→French-language works237,207→