Muscle fiber size, myonuclear domain, and fat mass phenotypes in pre-migratory snow buntings
Bibliographic record
Abstract
In long-distance migrants, preparation for migration is typically associated with increases in fat and body mass, and with an enlargement of pectoralis muscle mass that likely improves flight performance. Although changes in muscle mass or size have been well described in migratory birds, potential changes in muscle ultrastructure during this transition still deserves scrutiny. Using outdoor captive snow buntings (Plectrophenax nivalis n = 15) measured during their transition into a spring migratory phenotype as a model system, we studied changes in pectoralis muscle ultrastructure and predicted that muscle fiber diameter could increase in parallel with the gain in body mass. We also expected that larger fibers could either recruit satellite cells to support cellular maintenance and protein turnover, increase myonuclear domain (cytoplasm per nuclei) with the potential increase for protein turnover load per myonucleus, or existing myonuclei could undergo endoreduplication. Buntings increased body mass by 46% within a month, largely due to a > 6-fold increase in body fat. However, this increase in body mass was also associated with a 36% increase in muscle fiber diameter. Both pectoralis muscle mass (r2 = 0.57-0.77) and fiber diameter (r2 = 0.32) correlated with total body mass, without any change in the number of nuclei per fiber. Consequently, variation in myonuclear domain (i.e. the amount of cytoplasm per nucleus), was also positively associated with body mass (r2 = 0.51). Therefore, buntings preparing for migration may experience an increase in muscle contraction force due to larger muscle fibers, but this is also coupled with increases in myonuclear domain, which may force these cells to increase protein production to safeguard satellite cells.
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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 | 0.001 | 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".