Current progress in studying blackleg disease ( <i>Leptosphaeria maculans</i> and <i>L. biglobosa</i> ) of canola in Iran: Where do we stand now?
Bibliographic record
Abstract
Abstract Blackleg, caused by a complex of Leptosphaeria species ( L . maculans and L . biglobosa ), is a fungal disease on Brassica species, especially important in canola ( Brassica napus ). Since the first report of L . biglobosa in Iran in 2007 and L . maculans in 2008, both species are now of major importance in Iran affecting 10 provinces and 30 regions, with a higher prevalence in the northern provinces of Mazandaran and Golestan. Despite the rapid progression of the disease and the emergence of new Leptosphaeria races in Iran, the research into this pathogen has not progressed at the same rate and is limited to phenotypic characterization studies, pathogenicity research, and to a lesser extent, disease management research. Given the rapid increase in canola cultivation in Iran and changes in the genetic diversity of the pathogen populations, it is likely that blackleg disease will increasingly become a severe threat to Iran’s canola production. Therefore, systematic and prospective studies, along with fundamental research on the pathogen's biology, epidemiology, and genetic diversity, would provide critical information for the development of disease management strategies. Here, we review the research that has been carried out to date on blackleg disease in Iran and describe the extent of progress towards disease control, especially in disease‐prone regions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".