Occurrence of eyespot of cereals in Tunisia and identification of <i>Oculimacula</i> species and mating types
Bibliographic record
Abstract
The objectives of this study were to investigate the occurrence of eyespot in commercial spring cereal crops in four climatic areas in Tunisia and characterize the dominant species of Oculimacula responsible for the disease. A total of 294 wheat, barley and oat fields were surveyed for eyespot incidence and severity during four cropping seasons from 2010 to 2014. Eyespot was identified in 63.5% of the fields with an average incidence of 23.1% infected stems. The number of infected fields as well as the incidence and severity of disease increased significantly during the 5 years of the study. A significant difference in the occurrence of eyespot among the climatic regions was noted with the wetter areas having greater disease incidence. The highest incidence of eyespot was recorded in durum wheat and bread wheat fields, whereas barley was significantly less infected. The effect of previous crop on eyespot incidence was not significant. All 70 isolates collected in this study were identified as O. yallundae and among them, 39 were identified as MAT1-2 and 31 as MAT1-1. This study highlights the influence of climatic conditions on the distribution of eyespot in the cereal growing areas of Tunisia as well as the increasing occurrence of the disease. The predominance of O. yallundae is an important consideration in the choice of integrated management strategies for eyespot. The presence of both mating types of O. yallundae in similar proportions suggests that sexual reproduction may be occurring.
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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 | 0.001 |
| 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) | 0.001 | 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".