Development of <i>Oculimacula yallundae</i> and <i>O. acuformis</i> (eyespot) lesions on stems of winter wheat in relation to thermal time in the UK
Bibliographic record
Abstract
Relationships between development of eyespot, caused by Oculimacula yallundae (OY) or O. acuformis (OA) on stems of winter wheat (cv. Avalon), and thermal time (°C days after sowing) were investigated in field experiments in 1985/86, 1986/87 and 1987/88 (two experiments). In all experiments, the incidence and severity of stem eyespot (uninoculated plots, OY‐ and OA‐inoculated plots) were linearly related to accumulated thermal time after sowing. There were ca. 600–800°C days from the time of the first sample when eyespot lesions were recorded on stems to the time when maximum eyespot incidence or severity was recorded. Relationships between stem eyespot incidence or severity and thermal time differed between seasons, with more severe eyespot in 1986/87 and 1987/88 than 1985/86. Both the severity and volume of stem lesions were initially greater in OY‐inoculated plots than OA‐inoculated plots in spring but differences were less by harvest in all seasons. The percentage of plants with stems colonized by OY or OA over all plots (including uninoculated) showed a consistent pattern in 1986/87 and 1987/88 (two experiments), with the percentage colonized by OY greater initially and the percentage colonized by OA gradually increasing with time towards harvest.
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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 | 0.001 | 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".