Hypoxia Tolerance in Reptiles, Amphibians, and Fishes: Life with Variable Oxygen Availability
Bibliographic record
Abstract
The ability of fishes, amphibians, and reptiles to survive extremes of oxygen availability derives from a core triad of adaptations: profound metabolic suppression, tolerance of ionic and pH disturbances, and mechanisms for avoiding free-radical injury during reoxygenation. For long-term anoxic survival, enhanced storage of glycogen in critical tissues is also necessary. The diversity of body morphologies and habitats and the utilization of dormancy have resulted in a broad array of adaptations to hypoxia in lower vertebrates. For example, the most anoxia-tolerant vertebrates, painted turtles and crucian carp, meet the challenge of variable oxygen in fundamentally different ways: Turtles undergo near-suspended animation, whereas carp remain active and responsive in the absence of oxygen. Although the mechanisms of survival in both of these cases include large stores of glycogen and drastically decreased metabolism, other mechanisms, such as regulation of ion channels in excitable membranes, are apparently divergent. Common themes in the regulatory adjustments to hypoxia involve control of metabolism and ion channel conductance by protein phosphorylation. Tolerance of decreased energy charge and accumulating anaerobic end products as well as enhanced antioxidant defenses and regenerative capacities are also key to hypoxia survival in lower vertebrates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".