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Record W2056566894 · doi:10.1016/j.icesjms.2005.04.013

The emerging role of climate in post-smolt growth of Atlantic salmon

2005· article· en· W2056566894 on OpenAlexaffabout
Kevin D. Friedland, Gérald Chaput, Julian C. MacLean

Bibliographic record

VenueICES Journal of Marine Science · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsFisheries and Oceans Canada
Fundersnot available
KeywordsSalmoTributaryFisherySea surface temperatureBiologyFish <Actinopterygii>Fish stockClimate changeEnvironmental scienceEcologyOceanographyGeography

Abstract

fetched live from OpenAlex

Abstract Though it is well established that mortality during the post-smolt year is critical in shaping recruitment patterns in Atlantic salmon (Salmo salar L.), the controlling mechanism for North Atlantic stocks remains elusive. The emerging paradigm is based on relationships between survival and ocean thermal conditions during the early marine phase. The survival of post-smolts from both Europe and North America has been correlated to sea surface temperature conditions during their first month at sea. In addition, data for European fish support the hypothesis that growth mediates survival during that time. Thus, the working model for a salmon recruitment mechanism is analogous to those proposed for other fish species. We present growth data for two stocks, the Girnock Burn, which is a tributary to the River Dee, Scotland, and the Margaree River, Canada. Circuli spacing data for Girnock Burn fish suggest post-smolt growth is negatively correlated with temperature in the migration corridors during the first months at sea, while post-smolt growth of Margaree fish appears to be positively correlated with temperature. Growth is either patterned by a physiological response to an optimal temperature, or is co-varying with some ecosystem effect on growth, and the growth response might affect mortality and maturation of the stocks.

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)0.002
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 score0.024
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.004
GPT teacher head0.218
Teacher spread0.215 · 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

Citations95
Published2005
Admission routes2
Has abstractyes

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