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Record W4361270535 · doi:10.1002/cphy.c220003

The Mouse‐To‐Elephant Metabolic Curve: Historical Overview

2023· article· en· W4361270535 on OpenAlexaff
Jacopo P. Mortola

Bibliographic record

VenueComprehensive physiology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsMcGill University
Fundersnot available
KeywordsMetabolic rateContext (archaeology)BiologyFunction (biology)ZoologyPhysiologyEvolutionary biologyEndocrinology

Abstract

fetched live from OpenAlex

Although it is intuitive that large mammals need more food than smaller ones, it is not so obvious that, relative to their body mass, larger mammals consume less than smaller ones. In fact, on a per kg basis, the resting metabolic rate of a mouse is some 50 times higher than that of an elephant. The fact that metabolism could not be proportional to the mass of the animal was suggested by Sarrus and Rameaux in 1838. The first indication that oxygen consumption (or other indices of metabolic rate, Y ) related to the animal body mass ( M ) according to an exponential of the type Y = a · Mb, where b was about 0.75, was presented by Max Kleiber in 1932. Two years later Samuel Brody had collected sufficient data to construct the first “mouse-to-elephant” metabolic curve. The physiological basis of the relationship has been the object of many hypotheses, often accompanied by a great deal of controversy. This historical essay traces the origin of the mouse-to-elephant metabolic function, recalling the earliest concepts of metabolism and its measurements to understand the body size dependency, which is still one of the most elusive phenomena in comparative physiology. A brief look at the metabolic scaling of nonmammalian organisms will be included to frame the mouse-to-elephant curve into a broader context and to introduce some interesting interpretations of the mammalian function. © 2023 American Physiological Society. Compr Physiol 13:4513-4558, 2023.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.007

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 head0.061
GPT teacher head0.283
Teacher spread0.223 · 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.

Study designBench or experimental
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

Citations5
Published2023
Admission routes1
Has abstractyes

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