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Record W1990263469 · doi:10.1139/z10-039

Metabolic power budgeting and adaptive strategies in zoology: examples from scallops and fishThe present review is one of a series of occasional review articles that have been invited by the Editors and will feature the broad range of disciplines and expertise represented in our Editorial Advisory Board.

2010· article· en· W1990263469 on OpenAlexaffvenue
Helga Guderley, Hans‐Otto Pörtner

Bibliographic record

VenueCanadian Journal of Zoology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBiologyAdaptive valueCompromisePower (physics)EcologyOrganismReproduction

Abstract

fetched live from OpenAlex

Evolutionary explanations of the adaptive value of animal characteristics are often expressed in energetic terms, but unless they are accompanied by demonstrations of limited energy availability, they remain speculative. In this review, we argue that metabolic power budgeting provides easily testable mechanisms through which energetically efficient attributes could become adaptive. Given each organism’s maximal aerobic (and metabolic) capacity, available metabolic power (energy use per unit time) is limited and must be partitioned between different processes. This leads to compromises among the major fitness functions of growth, locomotor activity, and reproductive investment. As examples of such conflicts, we examine the compromise among growth, reproduction, and predator avoidance in scallops, as well as the means whereby thermal limitations on oxygen uptake reflect the geographical distribution limits and associated energetic trade-offs of temperate zone and polar fishes. These examples show several means whereby the budgeting of aerobic power is implicated in the major fitness trade-offs faced by 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 score0.391
Threshold uncertainty score0.999

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 head0.019
GPT teacher head0.242
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.

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

Citations116
Published2010
Admission routes2
Has abstractyes

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