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Heat dissipation limit theory and the evolution of avian functional traits in a warming world

2012· article· en· W2101358433 on OpenAlexfundno aff
David Grémillet, Laurence Meslin, Amélie Lescroël

Bibliographic record

VenueFunctional Ecology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsnot available
FundersCentre National de la Recherche ScientifiqueCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorMcGill UniversityNational Science Foundation
KeywordsBiologyPlumageEnergeticsThermoregulationMetabolic rateThermal management of electronic devices and systemsLimit (mathematics)Ecology

Abstract

fetched live from OpenAlex

Summary It is generally assumed that animal energy expenditure is limited by energy acquisition. In a series of publications, Speakman, Król and colleagues argue that the capacity to dissipate metabolic heat may also limit maximum rates of energy expenditure in endotherms (heat dissipation limit theory – HDL theory). The implications of the HDL theory for the evolution of avian functional traits are substantial and open fascinating research perspectives. Notably, the HDL theory leads us to (i) link elevated bird body temperatures with their capacity to achieve higher rates of heat loss and of energy expenditure, (ii) reconsider the evolution of avian plumage patterns and speculate upon the capacity of white birds to achieve higher field metabolic rates than darker relatives, (iii) hypothesize that the avian brood patch also functions as a thermal window allowing birds to shed excess heat and (iv) revise our current view of the adaptive significance of limited plumage thermal insulation in great cormorants. Such features have important implications for the capacity of birds to cope with global warming and for the design of mechanistic models of animal energetics aiming at predicting their responses to changing environmental conditions.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.207
Teacher spread0.191 · 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 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

Citations24
Published2012
Admission routes1
Has abstractyes

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