Out of the frying pan into the air—emersion behaviour and evaporative heat loss in an amphibious mangrove fish ( <i>Kryptolebias marmoratus</i> )
Bibliographic record
Abstract
Amphibious fishes often emerse (leave water) when faced with unfavourable water conditions. How amphibious fishes cope with the risks of rising water temperatures may depend, in part, on the plasticity of behavioural mechanisms such as emersion thresholds. We hypothesized that the emersion threshold is reversibly plastic and thus dependent on recent acclimation history rather than on conditions during early development. Kryptolebias marmoratus were reared for 1 year at 25 or 30°C and acclimated as adults (one week) to either 25 or 30°C before exposure to an acute increase in water temperature. The emersion threshold temperature and acute thermal tolerance were significantly increased in adult fish acclimated to 30°C, but rearing temperature had no significant effect. Using a thermal imaging camera, we also showed that emersed fish in a low humidity aerial environment (30°C) lost significantly more heat (3.3°C min(-1)) than those in a high humidity environment (1.6°C min(-1)). In the field, mean relative humidity was 84%. These results provide evidence of behavioural avoidance of high temperatures and the first quantification of evaporative cooling in an amphibious fish. Furthermore, the avoidance response was reversibly plastic, flexibility that may be important for tropical amphibious fishes under increasing pressures from climatic change.
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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".