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Record W2419863935 · doi:10.1139/cjfas-2015-0533

Condition dependence in the marine exit timing of sockeye salmon (<i>Oncorhynchus nerka</i>) returning to Copper Creek, Haida Gwaii

2016· article· en· W2419863935 on OpenAlexaffvenue
P. J. Katinic, David A. Patterson, Ronald C. Ydenberg

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsSimon Fraser UniversityFisheries and Oceans Canada
Fundersnot available
KeywordsOncorhynchusSpawn (biology)FisheryEnvironmental sciencePopulationEcologyResidence time (fluid dynamics)BiologyLife history theoryOceanographyLife historyFish <Actinopterygii>DemographyGeology

Abstract

fetched live from OpenAlex

We examined a small population of sockeye salmon (Oncorhynchus nerka) that enters their natal stream, to hold in their natal lake, months (>130 days) prior to spawning. This effectively decouples the influence of spawn timing requirements and behaviours from river entry (alternately referred to as “marine exit”) timing and is therefore a good model to study the migration strategies specifically associated with marine exit. We found individuals with early marine exit had higher growth rates in the months prior to river entry, had greater lipid density, were more likely male, more likely of the 2.2 versus 1.2 age class, had smaller gonads, and (if female) had more and smaller eggs. Body size at river entry did not vary seasonally. These patterns are explained using a life history model proposing that individual fish exit the sea when the marginal fitness benefits of further growth are outweighed by the marginal fitness cost of further marine residence. This point is reached at different times depending on body size, sex, lipid reserves, and the growth rate.

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 score0.979
Threshold uncertainty score0.042

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 head0.019
GPT teacher head0.227
Teacher spread0.208 · 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

Citations6
Published2016
Admission routes2
Has abstractyes

Explore more

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