Bibliographic record
Abstract
Freeze tolerance - the ability to survive internal ice formation - has evolved repeatedly in insects, facilitating survival in environments with low temperatures and/or high risk of freezing. Surviving internal ice formation poses several challenges because freezing can cause cellular dehydration and mechanical damage, and restricts the opportunity to metabolise and respond to environmental challenges. While freeze-tolerant insects accumulate many potentially protective molecules, there is no apparent 'magic bullet' - a molecule or class of molecules that appears to be necessary or sufficient to support this cold-tolerance strategy. In addition, the mechanisms underlying freeze tolerance have been minimally explored. Herein, we frame freeze tolerance as the ability to survive a process: freeze-tolerant insects must withstand the challenges associated with cooling (low temperatures), freezing (internal ice formation), and thawing. To do so, we hypothesise that freeze-tolerant insects control the quality and quantity of ice, prevent or repair damage to cells and macromolecules, manage biochemical processes while frozen/thawing, and restore physiological processes post-thaw. Many of the molecules that can facilitate freeze tolerance are also accumulated by other cold- and desiccation-tolerant insects. We suggest that, when freezing offered a physiological advantage, freeze tolerance evolved in insects that were already adapted to low temperatures or desiccation, or in insects that could withstand small amounts of internal ice formation. Although freeze tolerance is a complex cold-tolerance strategy that has evolved multiple times, we suggest that a process-focused approach (in combination with appropriate techniques and model organisms) will facilitate hypothesis-driven research to understand better how insects survive internal ice formation.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".