Susceptibility to Preharvest Sprouting of Chilean and Australian Elite Cultivars of Common Wheat
Bibliographic record
Abstract
One of the main factors that negatively affect common wheat (Triticum aestivum L.) grain quality is preharvest sprouting (PHS). In some regions of Chile and Australia, PHS can result in economic losses from lower yields and flour quality as a consequence of grains exposed to rain prior to harvest. Twenty‐three Chilean common wheat cultivars were characterized in two locations (central and southern Chile) using the falling number (FN) method, standard germination test, and visual sprouting for PHS screening in two experimental seasons (2013–2014 and 2014–2015). Similarly, 20 Australian wheat cultivars were compared in six seasons using germination index (GI) at harvest ripeness. In the Chilean study, there were high positive (GI and sprouting score [SS]) and negative (FNwet samples and SS) correlations between the PHS tests, suggesting that they are appropriate tools to differentiate the PHS response among the common wheat genotypes studied. There was a strong phenological stage and genotypic effect on PHS variation, and while most cultivars showed intermediate or susceptible PHS phenotype, ‘Konde’, ‘Kumpa’, and ‘Swindy’ exhibited PHS resistance. Likewise, the germination index of Australian cultivars ranged from high to intermediate. The effect of temperature on PHS susceptibility was investigated by comparing varieties in open field conditions and under polyethylene covers in Chile or by using different times of sowing in Australia. In both experiments, a positive correlation between temperature and susceptibility to PHS was observed, which was more pronounced in cultivars with intermediate PHS resistance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".