Large muscles are beneficial but not required for improving thermogenic capacity in small birds
Bibliographic record
Abstract
Abstract It is generally assumed that small birds improve their shivering heat production capacity by developing the size of their pectoralis muscles. However, some studies have reported an enhancement of thermogenic capacity in the absence of muscle mass variation between seasons or thermal treatments. We tested the hypothesis that an increase in muscle mass is not a prerequisite for improving avian thermogenic capacity. We measured basal (BMR) and summit (Msum) metabolic rates of black capped chickadees (Poecile atricapillus) acclimated to thermoneutral (27 °C) and cold (−10 °C) temperatures and obtained body composition data from dissections. Cold acclimated birds consumed 44% more food, and had 5% and 20% higher BMR and Msum, respectively, compared to individuals kept at thermoneutrality. However, lean dry pectoralis and total muscle mass did not differ between treatments, confirming that the improvement of thermogenic capacity did not require an increase in skeletal muscle mass. Nevertheless, within temperature treatments, Msum was positively correlated with the mass of all measured muscles, including the pectoralis. Therefore, for a given acclimation temperature individuals with large muscles do benefit from muscle size in term of heat production but improving thermogenic capacity during cold acclimation likely requires an upregulation of cell functions.
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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) | 0.002 | 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".