Freezing tolerance assessment for seedlings of three asparagus cultivars grown under controlled conditions
Bibliographic record
Abstract
Kim, J. and Wolyn, D. J. 2015. Freezing tolerance assessment for seedlings of three asparagus cultivars grown under controlled conditions. Can. J. Plant Sci. 95: 495-504. Asparagus (Asparagus officinalis L.) cultivars grown in southern Ontario must be winter-hardy. Development of a method to screen seedlings for freezing tolerance directly, or indirectly through metabolite analysis, could be useful in a breeding program. Ten-week-old seedlings of three cultivars with varying adaptation to southern Ontario, Guelph Millennium (GM), Jersey Giant (JG) and UC157 (UC), were acclimated under factorial combinations of two temperatures (7 or 23°C) and two photoperiods (8 and 16 h), with or without 5 additional days of sub-freezing acclimation at 3/-3°C (12/12 h) in darkness. Plants were then evaluated for metabolites and LT50, the temperature at which 50% of plants die. Photoperiod had no effect, but low temperature without sub-freezing acclimation decreased LT50 (increased freezing tolerance) of all three cultivars. The ranking of freezing tolerance, GM>JG>UC, was consistent with observed persistence in the field. The cultivars differed for concentrations of fern chlorophyll, and crown proline, high-molecular-weight fructan and sucrose, as well as crown percentage water. LT50 was highly correlated with crown percentage water, and chlorophyll, proline, sucrose, and high-molecular-weight fructan concentrations, suggesting these traits could be used as indirect measures to breed for winter-hardy cultivars.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".