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Metabolic correlates of leg length in breeding arctic shorebirds: the cost of getting high

2005· article· en· W2073938337 on OpenAlexaffabout
Ralph V. Cartar, R. I. G. Morrison

Bibliographic record

VenueJournal of Biogeography · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsCarleton UniversityUniversity of Lethbridge
Fundersnot available
KeywordsHomeothermyArcticEcologyEnergeticsBiologyFledgeWingRange (aeronautics)PredationThermoregulationEngineering

Abstract

fetched live from OpenAlex

Abstract Aim We test the hypothesis that tarsus length in all shorebirds breeding in the Canadian arctic shows an evolutionary response to average metabolic stress encountered across the breeding range, such that birds nesting in metabolically stressful environments have relatively shorter legs. Longer‐legged birds living in colder environments will experience greater metabolic costs because their torsos are elevated farther away from the ground's wind‐dampening boundary layer. Methods We use weather data (temperature, wind speed, global solar radiation) from 27 arctic weather stations measured over 37 years, and a previously published model of heat transfer, to characterize the metabolic harshness over the breeding season of the ranges of each of the 17 shorebirds of the family Charadriidae nesting in the Canadian arctic. Results After controlling for the lengths of two other body extremities (wing and bill), there was a significant negative relationship between tarsus length and mean metabolic harshness. This result was obtained whether species were treated as independent data points, or in a comparative analysis using standardized independent contrasts. Main conclusions We support a unique extension of Allen's rule: body‐supporting appendages of homeotherms may be shorter in colder environments so as to take advantage of a boundary layer effect, thereby reducing metabolic costs.

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)0.001
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 score0.013
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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

Citations36
Published2005
Admission routes2
Has abstractyes

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