Distribution and pathogenicity of <i>Cicer arietinum</i> infecting fungi in Tunisian agricultural lands
Bibliographic record
Abstract
Chickpea ( Cicer arietinum L.) ranks as one of the world’s leading legume crops, valued for its protein-packed edible seeds and contribution to soil fertility. However, chickpea cultivation encounters various biotic stresses, particularly fungal diseases, significantly impacting its productivity. In this study, a collection of 178 fungi, belonging to six geographical locations, was isolated from root and aerial tissues showing signs of fungal infection. The pathogenicity tests revealed that 75% of these fungi exhibited varying degrees of pathogenicity, with very pathogenic fungi representing the largest fraction (39%). Among these, 20 highly pathogenic fungal isolates were identified, representing eight different morphological types. Using rDNA ITS-sequencing, we classified these isolates into three genera and five distinct species, including four newly identified pathogens of chickpea: Fusarium foetens, Fusarium boothii, Macrophomina pseudophaseolina, and Aspergillus alliaceus. Molecular characterization and morphological analysis highlighted the prevalence of the genus Fusarium and the species F. foetens across all investigated sites. These results provide key insights into chickpea fungal pathogens, aiding crop management.
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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) | 0.000 | 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".