Revisiting concepts of thermal physiology: understanding negative feedback and set‐point in mammals, birds, and lizards
Bibliographic record
Abstract
The thermoregulatory system of homeothermic endotherms operates to attain thermal equilibrium, that is no net loss or gain of heat, where possible, under a thermal challenge, and not to attain a set-point or any other target body temperature. The concept of a set-point in homeothermic temperature regulation has been widely misinterpreted, resulting in such confusion that some thermoregulation specialists have recommended that it be abandoned. But the set-point concept has enjoyed a resurgence in a different domain, lizard microclimate selection. We review the principles of thermoregulation in homeotherms, endorse a negative feedback system with independent set-points for individual thermo-effectors as its core mechanism, and address the misconceptions about homeothermic set-point. We also explore the concept of set-point range in lizard microclimate selection and conclude that there is substantial convergence between that concept and the set-points of homeothermic thermo-effectors, as thresholds. In neither homeothermic nor lizard thermoregulation is the concept of a unitary set-point appropriate. We review the problems of measuring the set-points for lizard microclimate selection. We do not believe that the set-point concept in thermoregulation should be abandoned just because it has been misinterpreted by some users. It is a valid concept, identifying the threshold body temperatures at which regulatory thermo-effectors will be activated, to aid in attaining thermal equilibrium.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.009 |
| 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".