Measuring critical thermal maximum in aquatic ectotherms: a practical guide
Bibliographic record
Abstract
Critical thermal limits, commonly quantified as CTmax (maximum) or CTmin (minimum), are core metrics in the thermal biology of aquatic ectotherms. CTmax, in particular, has recently surged in popularity due to its various applications, including understanding and predicting the responses of animals to climate warming. Despite its growing popularity, there is a limited literature aimed at establishing best practices for designing, running, and reporting CTmax experiments. This lack of standardisation and insufficiently detailed reporting in the literature creates challenges when designing CTmax studies or comparing results across studies. Here, we provide a comprehensive, practical guide for designing and conducting experiments to measure critical thermal limits, with an emphasis on CTmax. Our recommendations cover 12 topic areas including apparatus design, masking (blinding), warming rates, endpoints, replication, and reporting. We include diagrams and photos for designing and building critical thermal limit arenas for field or lab applications. We also provide a reporting checklist as a reference for researchers when carrying out experiments and preparing manuscripts. Future studies incorporating critical thermal limits would benefit from transparent reporting of warming/cooling rates (raw data, supplementary graphs) and photo/video evidence showing arena designs and critical thermal limit endpoints. We also provide directions for empirical research that will help further inform the measurement of critical thermal limits, including on biotic factors like stress and digestion, warming/cooling rates, the effects of body mass on heat transfer, and the physiological mechanisms underlying thermal tolerance.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.064 | 0.052 |
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".