Freezing and desiccation tolerance of imbibed canola seed
Bibliographic record
Abstract
Depending on the environmental conditions, imbibed seeds survive subzero temperatures either by supercooling or by tolerating freezing‐induced desiccation. We investigated what the predominant survival mechanism is in freezing canola (Brassica napus cv. Quest) and concluded that it depends on the cooling rate. Seeds cooled at 3°C h−1 or faster supercooled, whereas seeds cooled over a 4‐day period to −12°C and then cooled at 3°C h−1 to−40°C did not display low temperature exotherms. Both differential thermal analysis and nuclear magnetic resonance (NMR) spectroscopy confirmed that imbibed canola seeds undergo freezing‐induced desiccation at slow cooling rates. The freezing tolerance of imbibed canola seed (LT50) was determined by slowly cooling to −12°C for 48 h, followed with cooling at 3°C h−1 to −40°C, or by holding at a constant −6°C (LD50). For both tests, the loss in freezing tolerance of imbibed seeds was a function of time and temperature of imbibition. Freezing tolerance was rapidly lost after radicle emergence. Seeds imbibed in 100 μM abscisic acid (ABA), particularly at 2°C, lost freezing tolerance at a slower rate compared with water‐imbibed seeds. Seeds imbibed in water either at 23°C for 16 h, or 8°C for 6 days, or 2°C for 6 days were not germinable after storage at −6°C for 10 days. Seeds imbibed in ABA at 23°C for 24 h, or 8°C for 8 days, or 2°C for 15 days were highly germinable after 40 days at a constant −6°C. Desiccation injury induced at a high temperature (60°C), as with injury induced by freezing, was found to be a function of imbibition temperature and time.
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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 | 0.001 |
| 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".