Constant temperature and fluctuating temperature have distinct effects on hypoxia tolerance in killifish (<i>Fundulus heteroclitus</i>)
Bibliographic record
Abstract
Climate change is leading to rapid change in aquatic environments, increasing the mean and variability of temperatures, and increasing the incidence of hypoxia. We investigated how acclimation to constant temperatures or to diel temperature fluctuations affects hypoxia tolerance in mummichog killifish (Fundulus heteroclitus). Killifish were acclimated to constant cool (15°C), constant warm (25°C) or a diel temperature cycle (15°C at night, 25°C during day) for 6 weeks. We then measured hypoxia tolerance (time to loss of equilibrium in severe hypoxia, tLOE; critical O2 tension, Pcrit), whole-animal metabolism, gill morphology, haematology and tissue metabolites at 15°C and 25°C in a full factorial design. Among constant temperature groups, tLOE was highest and Pcrit was lowest in fish tested at their acclimation temperature. Warm-acclimated fish had lower metabolic rate at 25°C and greater gill surface area (less coverage of lamellae by interlamellar cell mass, ILCM), but cool-acclimated fish had greater brain glycogen stores. Therefore, effects of constant temperature acclimation on hypoxia tolerance were temperature specific and not exhibited broadly across test temperatures, and they were associated with different underlying mechanisms. Hypoxia tolerance was less sensitive to test temperature in fish acclimated to fluctuating temperatures compared with fish acclimated to constant temperature. Acclimation to fluctuating temperatures also increased haemoglobin-O2 affinity of the blood (decreased P50) compared with constant temperature groups. Therefore, acclimation to fluctuating temperatures helps maintain hypoxia tolerance across a broader range of temperatures, and leads to some distinct physiological adjustments that are not exhibited by fish acclimated to constant temperatures.
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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 | 0.000 |
| 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".