The physiology of long-distance migration: extending the limits of endurance metabolism
Bibliographic record
Abstract
Long-distance migrants have evolved specific adaptations that make their athletic records possible. Unique mechanisms explaining their amazing capacity for endurance exercise have now been uncovered, particularly with respect to energy storage, mobilization, transport and utilization. Birds are champions of migration because flying offers a key compromise: it allows more rapid movement than swimming, but has a lower cost of transport than running. High efficiency for muscle contraction, pointed wings, low wingloading, travelling in V-formations, storing fuel as energy-dense lipids and atrophy of non-essential organs are some of their strategies to decrease the cost of transport. The ability to process lipids rapidly also emerges as a crucial component of the migrant phenotype. High lipid fluxes are made possible by lipoprotein shuttles and fatty acid binding proteins (FABPs) that accelerate lipid transport and by upgrading the metabolic machinery for lipolysis and lipid oxidation. Preparation for long flights can include natural doping on n-3 polyunsaturated fatty acids (n-3 PUFAs) from unique invertebrate diets. Muscle performance is improved by restructuring membrane phospholipids and by activating key genes of lipid metabolism through peroxisome proliferator-activated receptors (PPARs). The physiological secret to long migrations does not depend on a single ;magic' adaptation but on the integration of multiple adjustments in morphology, biomechanics, behavior, nutrition and metabolism. Research on the physiology of migrants improves the fundamental knowledge of exercise biology, but it also has important implications for wildlife conservation, treating obesity and improving the performance of human athletes.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".