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Record W1867148872 · doi:10.1890/es14-00162.1

Effects of subsidies from spawning chum and pink salmon on juvenile coho salmon body size and migration timing

2015· article· en· W1867148872 on OpenAlexafffundabout
Michelle C. Nelson, John D. Reynolds

Bibliographic record

VenueEcosphere · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsRaincoast Conservation FoundationSimon Fraser University
FundersRaincoast Conservation Foundation
KeywordsOncorhynchusJuvenileFisheryBiologySTREAMSChinook windHabitatFish migrationFish <Actinopterygii>Ecology

Abstract

fetched live from OpenAlex

Organisms transporting nutrients from highly productive ecosystems can subsidize food webs and alter ecosystem processes. For example, the carcasses and eggs of migratory Pacific salmon ( Oncorhynchus spp.) provide a high‐quality food source that could potentially benefit other species of salmon rearing in fresh water. We investigated relationships between spawning chum ( O. keta ) and pink ( O. gorbuscha ) salmon density, and the body size and age of juvenile coho salmon ( O. kisutch ) in 17 streams on the central coast of British Columbia, Canada. Chum salmon density was the most consistently important and positive correlate of coho body size, in comparison with pink salmon density, juvenile coho salmon density, and numerous characteristics of habitats. This was shown by comparisons both among and within streams, and between sites above and below natural barriers to spawning chum and pink salmon. In addition, streams that had higher chum and pink salmon spawning densities had a higher proportion of age 0 coho (less age 1), suggesting earlier juvenile coho salmon migration to the ocean with increased spawning salmon nutrient availability. Most of the coho salmon sampled had little or no direct contact with spawning chum and pink salmon, which suggests an indirect, time‐delayed influence on coho salmon body size.

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 score0.226
Threshold uncertainty score0.392

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 head0.008
GPT teacher head0.204
Teacher spread0.196 · 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

Citations14
Published2015
Admission routes3
Has abstractyes

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