Obese super athletes: fat-fueled migration in birds and bats
Bibliographic record
Abstract
Migratory birds are physiologically specialized to accumulate massive fat stores (up to 50-60% of body mass), and to transport and oxidize fatty acids at very high rates to sustain flight for many hours or days. Target gene, protein and enzyme analyses and recent -omic studies of bird flight muscles confirm that high capacities for fatty acid uptake, cytosolic transport, and oxidation are consistent features that make fat-fueled migration possible. Augmented circulatory transport by lipoproteins is suggested by field data but has not been experimentally verified. Migratory bats have high aerobic capacity and fatty acid oxidation potential; however, endurance flight fueled by adipose-stored fat has not been demonstrated. Patterns of fattening and expression of muscle fatty acid transporters are inconsistent, and bats may partially fuel migratory flight with ingested nutrients. Changes in energy intake, digestive capacity, liver lipid metabolism and body temperature regulation may contribute to migratory fattening. Although control of appetite is similar in birds and mammals, neuroendocrine mechanisms regulating seasonal changes in fuel store set-points in migrants remain poorly understood. Triacylglycerol of birds and bats contains mostly 16 and 18 carbon fatty acids with variable amounts of 18:2n-6 and 18:3n-3 depending on diet. Unsaturation of fat converges near 70% during migration, and unsaturated fatty acids are preferentially mobilized and oxidized, making them good fuel. Twenty and 22 carbon n-3 and n-6 polyunsaturated fatty acids (PUFA) may affect membrane function and peroxisome proliferator-activated receptor signaling. However, evidence for dietary PUFA as doping agents in migratory birds is equivocal and requires further study.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".