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Record W2023882005 · doi:10.1086/677219

Burst Swimming in Areas of High Flow: Delayed Consequences of Anaerobiosis in Wild Adult Sockeye Salmon

2014· article· en· W2023882005 on OpenAlexafffundabout
Nicholas J. Burnett, Scott G. Hinch, Douglas C. Braun, Matthew T. Casselman, Collin T. Middleton, Samantha M. Wilson, Steven J. Cooke

Bibliographic record

VenuePhysiological and Biochemical Zoology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsBiologyZoologyFishery

Abstract

fetched live from OpenAlex

Wild riverine fishes are known to rely on burst swimming to traverse hydraulically challenging reaches, and yet there has been little investigation as to whether swimming anaerobically in areas of high flow can lead to delayed mortality. Using acoustic accelerometer transmitters, we estimated the anaerobic activity of anadromous adult sockeye salmon (Oncorhynchus nerka) in the tailrace of a diversion dam in British Columbia, Canada, and its effects on the remaining 50 km of their freshwater spawning migration. Consistent with our hypothesis, migrants that elicited burst swimming behaviors in high flows were more likely to succumb to mortality following dam passage. Females swam with more anaerobic effort compared to males, providing a mechanism for the female-biased migration mortality observed in this watershed. Alterations to dam operations prevented the release of hypolimnetic water from an upstream lake, exposing some migrants to supraoptimal, near-lethal water temperatures (i.e., 24°C) that inhibited their ability to locate, enter, and ascend a vertical-slot fishway. Findings from this study have shown delayed post-dam passage survival consequences of high-flow-induced burst swimming in sockeye salmon. We highlight the need for studies to investigate whether dams can impose other carryover effects on wild aquatic animals.

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 score0.213
Threshold uncertainty score0.447

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 studies0.0000.001
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 head0.202
Teacher spread0.195 · 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

Citations111
Published2014
Admission routes3
Has abstractyes

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