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Record W1997761843 · doi:10.1139/f09-103

Egg consumption in mature Pacific salmon (Oncorhynchus spp.)

2009· article· en· W1997761843 on OpenAlexaffvenue
Shawn R. Garner, John W. Heath, Bryan D. Neff

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsYellow Island Aquaculture (Canada)Western University
Fundersnot available
KeywordsOncorhynchusSpawn (biology)FisheryBiologyChinook windHabitatNutrientFish migrationEcologySalmonidaeFish <Actinopterygii>Rainbow trout

Abstract

fetched live from OpenAlex

For over a century, conventional wisdom has maintained that Pacific salmon ( Oncorhynchus spp.) cease feeding when they return to freshwater rivers to spawn. Here, we overturn this belief by showing that multiple species of Pacific salmon consume eggs during spawning. Overall, 13% of stomachs contained eggs, and feeding rates were estimated at up to 14 eggs or 52 kJ per day. Feeding experiments revealed that mature salmon could digest eggs, as fed salmon maintained significantly higher body mass than unfed salmon. Such egg consumption presents a fundamental shift in our understanding of Pacific salmon ecology that may have important implications for migration and management models. Indeed, the energy from consumed eggs could allow salmon to migrate up to an additional 3.8 km upstream per day of feeding, or extend the duration of spawning activity by up to 12%. Furthermore, salmon stocks may have evolved local adaptations to their specific migration and spawning requirements that include nutrients from salmon eggs. Thus, successful salmon management efforts may need to preserve not only spawning habitat and migration corridors, but also feeding opportunities.

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 score0.025
Threshold uncertainty score0.050

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 head0.014
GPT teacher head0.214
Teacher spread0.200 · 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

Citations29
Published2009
Admission routes2
Has abstractyes

Explore more

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