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Record W2067726171 · doi:10.1139/z01-134

Influence of breeding habitat on bear predation and age at maturity and sexual dimorphism of sockeye salmon populations

2001· article· en· W2067726171 on OpenAlexvenueno aff
Thomas P. Quinn, Lisa A. Wetzel, Susan Bishop, Kristi Overberg, Donald E. Rogers

Bibliographic record

VenueCanadian Journal of Zoology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPredationBiologyOncorhynchusHabitatSexual maturitySexual dimorphismFecundityEcologyRange (aeronautics)FisheryZoologyFish <Actinopterygii>Population

Abstract

fetched live from OpenAlex

Age structure and morphology differ among Pacific salmon (Oncorhynchus spp.) populations. Sexual selection and reproductive capacity (fecundity and egg size) generally favor large (old), deep-bodied fish. We hypothesized that natural selection from physical access to spawning grounds and size-biased predation by bears, Ursus spp., opposes such large, deep-bodied salmon. Accordingly, size and shape of salmon should vary predictably among spawning habitats. We tested this hypothesis by measuring the age composition and body depth of sockeye salmon, Oncorhynchus nerka, and the intensity of predation in a range of breeding habitats in southwestern Alaska. Stream width was positively correlated with age at maturity and negatively correlated with predation level. However, salmon spawning on lake beaches were not consistently old, indicating that different factors affect age in riverine- and beach-spawning populations. Body depths of male and female salmon were positively correlated with water depth across all sites, as predicted. However, the mouths of some streams were so shallow that they might select against large or deep-bodied salmon, even in the absence of bear predation. Taken together, the results indicated that habitat has direct and indirect effects (via predation) on life history and morphology of mature salmon.

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 score0.245
Threshold uncertainty score0.766

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 studies0.0000.000
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 head0.017
GPT teacher head0.221
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 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

Citations140
Published2001
Admission routes1
Has abstractyes

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