The role of drought stress on the acquisition of freezing tolerance in asparagus under controlled conditions
Bibliographic record
Abstract
Cold acclimation induces freezing tolerance in asparagus ( Asparagus officinalis L.), which is important for survival and longevity in temperate climates. Previous studies indicated that cultivar “Guelph Millennium” (GM), adapted to Southern Ontario, acquired freezing tolerance earlier in the fall and maintained it later in the spring than “UC157” (UC), a cultivar commonly grown in desert climates. As the drought and cold acclimation metabolic pathways overlap, the objective of this research was to determine whether drought alone induced freezing tolerance or interacted synergistically with cold temperatures. Seedlings of GM and UC were subjected to control, drought, or cold acclimating conditions for 6 weeks. Additional treatments included 6 weeks of drought or control conditions followed by 6 weeks of cold acclimation, and plants grown for 12 weeks under control conditions. LT50, the temperature at which 50% of plants die, and metabolites or parameters associated with freezing tolerance were measured. GM had decreased LT50 levels (increased freezing tolerance) compared with UC under both drought and cold acclimating conditions, and no cultivar differences were observed under control conditions. Drought stress before cold acclimation resulted in an increased LT50 (reduced freezing tolerance) for UC compared with GM. A high root:shoot ratio and low crown water percentage were associated with increased freezing tolerance in GM, while a high crown sucrose concentration in UC was related to decreased freezing tolerance for the drought treatment. Overall, drought stress induced cultivar-specific adaptations causing differences in freezing tolerance, thus confirming the interrelationship between these two physiological pathways.
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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) | 0.000 | 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".