The energy cost of feather replacement is not intrinsically inefficient
Bibliographic record
Abstract
Feathers serve a diversity of functions in birds and their continuous use and exposure to the environment requires a scheduled moult to maintain their full functionality. As feathers represent about 25% of a bird’s protein content, moult is expected to impose substantial energy and nutrient demands, but perhaps not to the extent reported. Energy conversion efficiencies for feather formation are among the lowest for any biological structure examined, but this assumes that increases in maintenance energy requirements (minimum resting metabolic rate (RMRmin)) during moult are predominately due to feather synthetic costs. We tested this assumption by comparing the RMRmin and protein turnover rates of House Sparrows (Passer domesticus (Linnaeus, 1758)) during peak moult and in a non-moulting cohort before and 12 days after having a similar amount of feathers plucked. Replacement of plucked feathers had no effect on metabolic rate, whereas RMRmin was 28% higher in moulting than in non-moulting House Sparrows. Protein turnover rates were lowest in non-moulting birds, but rate differences between non-moulting and moulting birds were threefold higher than those between non-moulting and plucked House Sparrows. Thus, the energy inefficiencies reported for feather replacement are mainly due to costs associated with coincident processes rather than being a direct cost of feather synthesis per se.
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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 | 0.000 |
| 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 | 0.000 | 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".