Muscle fiber structure in an aging long‐lived seabird, the black‐legged kittiwake (<scp><i>Rissa tridactyla</i></scp>)
Bibliographic record
Abstract
Many long-lived animals do not appear to show classic signs of aging, perhaps because they show negligible senescence until dying from "catastrophic" mortality. Muscle senescence is seldom examined in wild animals, yet decline in muscle function is one of the first signs of aging in many lab animals and humans. Seabirds are an excellent study system for physiological implications of aging because they are long-lived animals that actively forage and reproduce in the wild. Here, we examined linkages between pectoralis muscle fiber structure and age in black-legged kittiwakes (Rissa tridactyla). Pectoralis muscle is the largest organ complex in birds, and responsible for flight and shivering. We obtained and fixed biopsies from wild black-legged kittiwakes of known age. We then measured muscle fiber diameter, myonuclear domain and capillaries per fiber area among birds of differing ages. All muscle parameters were independent of age. Number of nuclei per mm of fiber showed a positive correlation with muscle fiber cross-sectional area, and myonuclear domain increased with muscle fiber diameter. Thus, as muscle fibers increased in size, they may not have recruited satellite cells, increasing the protein turnover load per nuclei. We conclude that senescence in a long-lived bird with an active lifestyle, does not entail mammalian-like changes in muscle structure.
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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".