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Record W2053460502 · doi:10.1139/f04-215

Factors affecting marine production of Atlantic salmon (<i>Salmo salar</i>)

2004· article· en· W2053460502 on OpenAlexvenueno aff
Bror Jönsson, Nina Jönsson

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSalmoSexual maturityBiologyFisheryPredationAbundance (ecology)Maturity (psychological)PopulationGrowth rateEcologyFish <Actinopterygii>Demography

Abstract

fetched live from OpenAlex

This paper reviews recent advances in our understanding of factors influencing the marine production of wild Atlantic salmon (Salmo salar). Population abundance has declined during the last 30 years because of decreased marine growth rate, survival-rate, and production of multi-sea-winter fish. Mortality appears density-independent, indicating that the marine abundance is beneath the carrying capacity for the species. Correlations between the North Atlantic Oscillation winter index for the post-smolt year and production variables indicate that unfavourable climatic conditions are partly responsible for the decline. Low sea temperature may be the ultimate reason for the poor salmon production, whereas predation is one proximate mortality factor, which is probably both size and temperature dependent. Low growth rate during cold years was associated with low sea age at maturity and small salmon, contrary to the common observation that fast growth leads to young age at maturity. It is suggested that low water temperature may stimulate lipid storage relative to protein production and that the energy density needed to attain sexual maturity is lower in small than in large salmon. Future research should focus on the relationships between smolt age and (or) size and adult age and (or) size, and the association among water temperature, growth rate, growth efficiency, and age at sexual maturity.

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 score0.131
Threshold uncertainty score0.996

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 communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.205
Teacher spread0.189 · 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

Citations147
Published2004
Admission routes1
Has abstractyes

Explore more

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