Move That Fatty Acid: Fuel Selection and Transport in Migratory Birds and Bats
Bibliographic record
Abstract
The metaphor of marathon running is inadequate to fully capture the magnitude of long-distance migratory flight of birds. In some respects a journey to the moon seems more appropriate. Birds have no access to supplementary water or nutrition during a multi-day flight, and they must carefully budget their body fat and protein stores to provide both fuel and life support. Fatty acid transport is crucial to successful non-stop migratory flight in birds. Although fat is the most energy-dense metabolic fuel, the insolubility of its component fatty acids makes them difficult to transport to working muscles fast enough to support the highly aerobic exercise required to fly. Recent evidence indicates that migratory birds compensate for this by expressing large amounts of fatty acid transport proteins on the membranes of the muscles (FAT/CD36 and FABPpm) and in the cytosol (H-FABP). Through endogenous mechanisms and/or diet, migratory birds may alter the fatty acid composition of the fat stores and muscle membranes to improve endurance during flight. Fatty acid chain length, degree of unsaturation, and placement of double bonds can affect the rate of mobilization of fatty acids from adipose tissue, utilization of fatty acids by muscles, and whole-animal performance. However, there is great uncertainty about how important fatty acid composition is to the success of migration or whether particular types of fatty acids (e.g., omega-3 or omega-6) are most beneficial. Migratory bats provide an interesting example of evolutionary convergence with birds, which may provide evidence for the generality of the bird model to the evolution of migration by flight in vertebrates. Yet only recently have attempts been made to study bat migration physiology. Many aspects of their fuel metabolism are predicted to be more similar to those of migrant birds than to those of non-flying mammals. Bats may be distinct from most birds in their potential to conserve energy by using torpor between flights, and in the behavioral and physiological trade-offs they may make between migration and reproduction, which often overlap.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".