Bibliographic record
Abstract
Sub-zero temperatures put plants at risk of damage associated with the formation of ice crystals in the apoplast. Some freeze-tolerant plants mitigate this risk by expressing ice-binding proteins (IBPs), that adsorb to ice crystals and modify their growth. IBPs are found across several biological kingdoms, with their ice-binding activity and function uniquely suited to the lifestyle they have evolved to protect, be it in fishes, insects or plants. While IBPs from freeze-avoidant species significantly depress the freezing point, plant IBPs typically have a reduced ability to lower the freezing temperature. Nevertheless, they have a superior ability to inhibit the recrystallization of formed ice. This latter activity prevents ice crystals from growing larger at temperatures close to melting. Attempts to engineer frost-hardy plants by the controlled transfer of IBPs from freeze-avoiding fish and insects have been largely unsuccessful. In contrast, the expression of recombinant IBP sequences from freeze-tolerant plants significantly reduced electrolyte leakage and enhanced freezing survival in freeze-sensitive plants. These promising results have spurred additional investigations into plant IBP localization and post-translational modifications, as well as a re-evaluation of IBPs as part of the anti-stress and anti-pathogen axis of freeze-tolerant plants. Here we present an overview of plant freezing stress and adaptation mechanisms and discuss the potential utility of IBPs for the generation of freeze-tolerant crops.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".