Dissecting cause from consequence: a systematic approach to thermal limits
Bibliographic record
Abstract
Thermal limits mark the boundaries of ectotherm performance, and are increasingly appreciated as strong correlates and possible determinants of animal distribution patterns. The mechanisms setting the thermal limits of ectothermic animals are under active study and rigorous debate as we try to reconcile new observations in the lab and field with the knowledge gained from a long history of research on thermal adaptation. Here, I provide a perspective on our divided understanding of the mechanisms setting thermal limits of ectothermic animals. I focus primarily on the fundamental differences between high and low temperatures, and how animal form and environment can place different constraints on different taxa. Together, complexity and variation in animal form drive complexity in the interactions within and among levels of biological organization, creating a formidable barrier to determining mechanistic cause and effect at thermal limits. Progress in our understanding of thermal limits will require extensive collaboration and systematic approaches that embrace this complexity and allow us to separate the causes of failure from the physiological consequences that can quickly follow. I argue that by building integrative models that explain causal links among multiple organ systems, we can more quickly arrive at a holistic understanding of the varied challenges facing animals at extreme 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".