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Record W2979135210 · doi:10.1242/jeb.213348

Amphibious fish ‘get a jump’ on terrestrial locomotor performance after exercise training on land

2019· article· en· W2979135210 on OpenAlexafffund
William McFarlane, Giulia S. Rossi, Patricia A. Wright

Bibliographic record

VenueJournal of Experimental Biology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTraining (meteorology)JumpFish <Actinopterygii>Physical medicine and rehabilitationEnvironmental scienceFisheryBiologyMedicineGeographyMeteorologyPhysics

Abstract

fetched live from OpenAlex

Many amphibious fishes rely on terrestrial locomotion to accomplish essential daily tasks, but it is unknown if terrestrial exercise improves the locomotor performance of fishes on land. Thus, we tested the hypothesis that terrestrial exercise improves locomotion in amphibious fishes out of water due to skeletal muscle remodeling. We compared the jumping performance of Kryptolebias marmoratus before and after an exercise training regimen, and assessed the muscle phenotype of control and exercise-trained fish. We found that exercise-trained fish jumped 41% farther, and 48% more times before reaching exhaustion. Furthermore, exercise training resulted in the hypertrophy of red muscle fibers, and an increase in red muscle capillarity and aerobic capacity. Lactate accumulation after jumping indicates that white muscle is also important in powering terrestrial jumps. Overall, skeletal muscle in K. marmoratus is highly responsive to terrestrial exercise, and muscle plasticity may assist in the effective exploitation of terrestrial habitats by amphibious fishes.

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)0.000
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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)0.0040.000

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 head0.020
GPT teacher head0.253
Teacher spread0.233 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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