Heat transfer in fish: are short excursions between habitats a thermoregulatory behaviour to exploit resources in an unfavourable thermal environment?
Bibliographic record
Abstract
Temperature is the primary environmental factor affecting physiological processes in ectotherms. Heat-transfer models describe how the fish's internal temperature responds to a fluctuating thermal environment. Specifically, the rate coefficient (k), defined as the instantaneous rate of change in body temperature in relation to the difference between ambient and body temperature, summarizes the combined effects of direct thermal conduction through body mass, passive convection (intracellular and intercellular fluids) and forced convective heat transfer (cardiovascular system). The k-coefficient is widely used in fish ecology to understand how body temperature responds to changes in water temperature. The main objective of this study was to estimate the k-coefficient of brook charr equipped with internal temperature-sensitive transmitters in controlled laboratory experiments. Fish were first transferred from acclimation tanks (10°C) to tanks at 14, 19 or 23°C (warming experiments) and were then returned to the acclimation tanks (10°C; cooling experiments), thus producing six step changes in ambient temperature. We used non-linear mixed models to estimate the k-coefficient. Model comparisons indicated that the model incorporating the k-coefficient as a function of absolute temperature difference (dT: 4, 9 and 13°C) best described body temperature change. By simulating body temperature in a heterogeneous thermal environment, we provide theoretical predictions of maximum excursion duration between feeding and resting areas. Our simulations suggest that short (i.e. <60 min) excursions could be a common thermoregulatory behaviour adopted by cold freshwater fish species to sustain body temperature below a critical temperature threshold, enabling them to exploit resources in an unfavourable thermal environment.
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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 | 0.001 |
| 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".