A lack of repeatability creates the illusion of a trade-off between basal and plastic cold tolerance
Bibliographic record
Abstract
The thermotolerance–plasticity trade-off hypothesis predicts that ectotherms with greater basal thermal tolerance have a lower acclimation capacity. This hypothesis has been tested at both high and low temperatures but the results often conflict. If basal tolerance constrains plasticity (e.g. through shared mechanisms that create physiological constraints), it should be evident at the level of the individual, provided the trait measured is repeatable. Here, we used chill-coma onset temperature and chill-coma recovery time (CCO and CCRT; non-lethal thermal limits) to quantify cold tolerance ofDrosophila melanogasteracross two trials (pre- and post-acclimation). Cold acclimation improved cold tolerance, as expected, but individual measurements of CCO and CCRT in non-acclimated flies were not (or only slightly) repeatable. Surprisingly, however, there was still a strong correlation between basal tolerance and plasticity in cold-acclimated flies. We argue that this relationship is a statistical artefact (specifically, a manifestation of regression to the mean; RTM) and does not reflect a true trade-off or physiological constraint. Thermal tolerance trade-off patterns in previous studies that used similar methodology are thus likely to be impacted by RTM. Moving forward, controlling and/or correcting for RTM effects is critical to determining whether such a trade-off or physiological constraint exists.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".