Metabolic correlates of leg length in breeding arctic shorebirds: the cost of getting high
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".