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Gait transition speed as an alternate measure of maximum aerobic capacity in fishes

2008· article· en· W1998859285 on OpenAlexaff
Stephan J. Peake

Bibliographic record

VenueJournal of Fish Biology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsRacewayGaitRespirometerBiologySalvelinusFish measurementMeasure (data warehouse)TroutAcclimatizationJuvenilePreferred walking speedFish <Actinopterygii>StatisticsEcologyFisheryMathematicsPhysicsAnatomyComputer scienceRespirationPhysiology

Abstract

fetched live from OpenAlex

This study demonstrated that the transition from a steady to an unsteady locomotory gait (USTmax) in juvenile brook trout Salvelinus fontinalis can be measured easily using a new tilting raceway design and a simple experimental protocol. It was found that USTmax increased linearly with fork length (LF), and that this relationship was statistically identical in fish that swam volitionally in the raceway and those that were forced to perform, although slightly different data processing methods were needed in the latter to achieve this result. Furthermore, the relationship between LF and USTmax was statistically identical to that between LF and critical swimming speed (Ucrit), although LF in the former relationship explained 83% of the variance compared to 37% in the latter. This finding indicates that gait transition speed can be used to estimate maximum aerobic capacity, with less unexplainable variance than Ucrit. Gait transition speeds were also determined from Ucrit tests; however, this required measuring and incorporating ground speed into the analysis. USTmax as determined in the Ucrit tests was not significantly different from that measured in the raceway, suggesting that gait transition speed can be measured in raceways or swim tunnel respirometers.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.032
GPT teacher head0.230
Teacher spread0.198 · 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

Citations37
Published2008
Admission routes1
Has abstractyes

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