Sex-dependent latent chilling injury changes estimates of thermal tolerance in a model insect
Bibliographic record
Abstract
Abstract Thermal tolerance limits are closely associated with insect distribution. Thermal injury sets limits to mobility or survival after removal from the stress, and these limits are frequently integrated into models describing or predicting climate suitability for species of interest. Cold stress severity, sex, and prior thermal acclimation status can all influence lower thermal limits. There is a growing understanding of chilling injuries initially manifest, but we poorly understand repair or further injury that may happen after rewarming. We exposed male and female Drosophila melanogaster to an acute or chronic cold stress before assessing their mobility over a 24 h period. Females progressively worsened under both conditions, but male mobility neither worsened nor improved. Female mobility declined slower in flies recovering at cooler temperatures, and cold acclimation significantly mitigated latent injury in females following the same degree of initial injury, regardless of recovery temperature. We conclude that latent chilling injury can be sex-specific, occurs independently from mechanisms driving tissue damage in the cold, is temperature-dependent, and is mitigated by prior thermal acclimation. We argue that latent chilling injury and the factors that influence it should be more carefully considered in estimating tolerance limits.
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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 | 0.000 |
| 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".