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Record W4252408696 · doi:10.1242/jeb.056796

SALMON HEARTS DO NOT FOLLOW MASS SCALING LAW

2011· article· en· W4252408696 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of Experimental Biology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsnot available
Fundersnot available
KeywordsScalingFisheryBiologyMathematicsGeometry

Abstract

fetched live from OpenAlex

Most large creatures have much lower heart rates than smaller a nimals. In fact, it is even possible to find a correlation between body size and heart rate, which has led some scientists to propose a model that could explain this phenomenon. But do all animals conform to this scaling law? Mounting evidence suggests that this may not always be the case. Timothy Clark and Tony Farrell from the University of British Columbia, Canada, point out that little is known about the scaling relationship between body mass and heart rate in fish. Curious to find out whether fish heart rate patterns follow the same scaling trend as other vertebrates, Clark and Farrell caught nine male Chinook salmon, ranging from 2.5 to 15.9 kg, as they migrated to their spawning grounds and implanted data loggers that could record each fish's heart rate and activity patterns. Next, Clark and Farrell allowed the animals to swim freely in a holding channel for 9 days before retrieving the data loggers (p. 887).However, when they analysed the data, there was no correlation between the fish's body size and heart rate. Instead of decreasing with size, the fish's resting heart rates were all between 30 and 43 beats min–1. And when the duo considered heart rate data from the literature for tiny 0.02–0.05 g trout, they found that even the smaller fish only had heart rates of 50–60 beats min–1, rather than the 1000 beats min–1 range you would expect if they followed the same size scaling pattern as birds and mammals.Next, the duo analysed the relationship between the fish's body size and various blood parameters, such as haemoglobin and glucose levels. They found that all of the parameters varied, but in no consistent way relative to the fish's body masses. Meanwhile, the fish's heart chamber (ventricle), spleen and heart muscle (myocardium) masses all scaled in proportion to the fish's body masses. The duo suggest that the Chinook salmon's ability to transport oxygen and their mass-specific cardiac output and cardiac power may be maintained across all body masses.Having shown that the scaling law for heart rate does not hold in Chinook salmon, Clark and Farrell point out that fish offer great potential for examining the effects of body mass on other physiological variables, as their body masses vary to a greater degree than any other vertebrates and they account for over half of all vertebrate species.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.045
GPT teacher head0.271
Teacher spread0.226 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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