Freezing Tolerance of Winter Canola Cultivars is Best Revealed by a Prolonged Freeze Test
Bibliographic record
Abstract
Small but important differences in winter survival are known to exist between winter canola cultivars. The objective of this study was to compare a short‐term (lethal temperature for 50% plant kill [LT50]) and a prolonged (lethal duration time for 50% plant kill [LD50]) freeze test to identify differences in freezing tolerance of winter canola (Brassica napus L. var. oleifera Metzg. and Brassica rapa L. var. oleifera Sink.) species and cultivars. Viability following each freeze test was determined by electrolyte leakage, plant survival, biomass of shoot regrowth, and root regeneration. Plant survival from the LT50 (cooling rate 3°C h−1) and LD50 (−8°C isothermal for up to 24 d) tests were poorly correlated, and only the LD50 test was able to identify cultivar differences in freezing tolerance. Electrolyte leakage did not correlate with actual survival measurements in both freeze tests. Shoot regrowth was a more sensitive viability test than plant survival. Brassica rapa plants subjected to the LT50 test showed greater shoot regrowth than B. napus plants, yet no cultivar differences were found within each species. In the LD50 freeze test, no difference in shoot regrowth was detected between the species; however, cultivar differences were found within both species. Tolerance to prolonged freezing is critical for winter survival, and the LD50 freeze test may allow more accurate screening for species and cultivars with improved freezing tolerance than the LT50 freeze test.
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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) | 0.001 | 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".