Improved methods to estimate days and temperature to fifty percent mortality of winter wheat (<i>Triticum aestivum</i> L.) under low-temperature flooding and ice encasement
Bibliographic record
Abstract
Low-temperature flooding and ice encasement (LTFIE) cause variable survival of winter wheat ( Triticum aestivum L.) in Ontario, which limits the adoption of wheat into crop rotations by growers. The development of novel cultivars capable of withstanding LTFIE is a promising avenue for improvement, but the methods used to assess the survival of winter wheat under LTFIE are restricted. This study developed updated methods to determine the survival of wheat cultivars under LTFIE using controlled environments and, to our knowledge, is the first method since the 1980s to use Canadian eastern soft red winter wheat (CESRW) to conduct cold tolerance studies. Chamber-acclimated plants of AC Carberry (spring wheat control), Branson (CESRW), CM614 (CESRW), and Norstar (hardy Canadian western red winter control) cultivars were used to estimate the days (LD50) and temperature (LT50) to reach 50% mortality under ice and without ice treatments. Norstar had the longest LD50 at 33 days, Branson and CM614 had similar LD50 of 18 and 20 days, and AC Carberry did not reach an LD50 as it died early in both treatments. The LT50 of each cultivar was different; Norstar had the lowest LT50 (−13.6 °C day 0 and −13.2 °C day 7), and AC Carberry had the highest LT50 (−6.6 °C day 0 and −2.7 °C day 7). The detailed methods developed in this study were more reliable compared to older methods based on the more accurate reported LD50 and LT50 of the cultivars, therefore, these methods can be used to screen winter cereals for LTFIE in the future.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".