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Record W2119934097 · doi:10.1139/f00-238

Optimal swimming speeds and forward-assisted propulsion: energy-conserving behaviours of upriver-migrating adult salmon

2000· article· en· W2119934097 on OpenAlexfundvenueno aff
Scott G. Hinch, Peter S. Rand

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOncorhynchusFish migrationFisheryEnvironmental scienceWatershedEnergy expenditureEcologyFish <Actinopterygii>BiologyComputer science

Abstract

fetched live from OpenAlex

Anadromous salmon migrations are energetically expensive. Long-distance migrants should be efficient in their use of energy and minimize swimming costs wherever possible. We explore swimming strategies and energy-saving tactics employed by three long-distance-migrating sockeye salmon (Oncorhynchus nerka) stocks in the Fraser River watershed, British Columbia. We used stereovideography and bank-side observations to estimate swimming speeds (from tailbeat frequency) and ground speeds (using distance traveled and duration) for individuals at several sites. Salmon were highly efficient at migration (i.e., ground speeds equaled or exceeded swimming speeds) through reaches with relatively low encountered currents (&lt;0.25 m·s -1 ). We speculate that salmon exploit small reverse-flow vortices to achieve this feat. With low encountered currents, most salmon migrated according to an optimal swimming speed model: migrants minimized transport costs per unit distance traveled. Generally, salmon were less efficient at migration with fast currents, although the Chilko stock were superoptimal migrants, possibly owing to unique morphology and (or) behaviours. The risk of significant delays is enhanced when fast currents are encountered. Under these conditions, relatively fast swimming speeds could minimize travel time, despite high costs. Migrants may be balancing energetic costs of migration against the fitness costs of spawning delays.

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 score0.099
Threshold uncertainty score0.992

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 studies0.0010.001
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 head0.010
GPT teacher head0.200
Teacher spread0.190 · 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

Citations180
Published2000
Admission routes2
Has abstractyes

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