Freezing Tolerance Attributes during Spring Deacclimation for Three Asparagus Cultivars with Varying Adaptation to Southern Ontario
Bibliographic record
Abstract
Winterhardiness in asparagus ( Asparagus officinalis ) may be related to proper cold acclimation and induction of freezing tolerance in the fall, levels and maintenance of freezing tolerance in the winter, and the timing of deacclimation in the spring. Premature deacclimation and the inability to reacclimate could result in crown damage from spring freeze-thaw cycles. A field experiment was conducted, replicated over 2 years, to determine how three cultivars with varying adaptation to southern Ontario deacclimate in the spring by assessing LT 50 (the temperature at which 50% of plants die) and biochemical and physiological parameters associated with freezing tolerance. ‘UC 157’ (UC), the least-adapted cultivar, deacclimated after soil temperatures rose above freezing; LT 50 values increased linearly over time and were unaffected by fluctuations in soil temperature. ‘Jersey Giant’ (JG), a cultivar with moderate adaptation, rapidly deacclimated with increased soil temperature but appeared to partially reacclimate as temperatures decreased. For ‘Guelph Millennium’ (GM), the most-adapted cultivar, LT 50 values did not change, maintaining the greatest levels of freezing tolerance during the spring sampling period. Although LT 50 values did not differ among cultivars on the first spring sampling date, ranking for freezing tolerance at the final sampling in each year was GM>JG>UC, which is consistent with adaptation. Rhizome traits were most associated with freezing tolerance and included high concentrations of low-molecular-weight fructans (LFs), glucose, and proline and low percentage water and sucrose concentration. Overall, data suggest that the timing of deacclimation and loss of freezing tolerance in the spring may significantly affect winterhardiness; cultivars that lose freezing tolerance early and cannot reacclimate could suffer most from late spring freeze-thaw cycles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".