Cold acclimation and prospects for cold-resilient crops
Bibliographic record
Abstract
Low-temperatures pose extreme challenges to crops causing significant economical impacts. Frosts are responsible for more than 30% of weather-related insured crop losses in some temperate climate jurisdictions, but are particularly devastating for small holdings and communities reliant on a bountiful harvest. Low-temperatures are also frequently accompanied by other abiotic and biotic stresses, including pathogen attacks. Some pathogens have sub-zero temperature optima, while others leverage low-temperatures to promote freezing at high sub-zero temperatures by way of ice-nucleating proteins in order to access intracellular nutrients. To survive low-temperatures and the attendant risks, various plant species have evolved complex and intricate signaling networks, molecular mechanisms, and physiological changes, in addition to symbiotic relationships with microbiota. Enhancing low-temperature survival and pathogen-induced freezing tolerance in cold susceptible, agriculturally significant crops is an attractive area of research with immense translatable value to all aspects of society. This area of research will be particularly important in our near future as climate change increases the unpredictability of frosts, particularly in the spring and autumn. Against this backdrop, the world population continues to grow while arable land remains finite and wealth inequality exacerbates food poverty. In this review, we examine plant (i) low-temperature stress, (ii) cold acclimation responses , particularly in crops (iii) antifreeze proteins, and (iv) frost-associated pathogens. Lastly, we suggest integrated approaches to improve crop frost 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".