Comparison of Different Methods for Phenotyping Preharvest Sprouting in White‐Grained Wheat
Bibliographic record
Abstract
ABSTRACT The objective of this study was to identify a suitable method for phenotyping preharvest sprouting (PHS) resistance in white bread wheat. Forty doubled‐haploid (DH) lines derived from a cross between two white‐grained spring wheats (Triticum aestivum L.) cultivar Argent (nondormant) and wheat breeding line W98616 (dormant) were evaluated for germination frequency, Falling Number (FN), and α‐amylase activity in dry and water‐imbibed seeds and spikes. The α‐amylase activity in dry seeds or spikes did not differ significantly between parent lines or lines of the DH population. Wetting of seeds or spikes for two days caused a five‐ to sevenfold increase in α‐amylase activity but only in Argent and the nondormant subgroup (49–100% germination) of the DH lines. A positive association (r = 0.60***) was detected between germination frequency and α‐amylase activity in imbibed seeds and spikes. Germination frequency could not be correlated to FN or α‐amylase activity in dry‐harvested seeds. FN showed a strong correlation (r = –0.83***) to α‐amylase activity in the dry‐harvested seeds but could not be correlated to α‐amylase activity in the imbibed seeds. The germination test was the most reliable method for measuring PHS resistance because seed dormancy provides potential resistance to PHS, whereas high α‐amylase activity may occur in grains without causing PHS.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".