Characterization and Incidence of Target Spot Lesions in Unifoliate Leaves, Petioles, and Stems of Soybean Cultivars
Bibliographic record
Abstract
This study aimed to classify the different types of leaf lesions caused by target spot and quantify their incidence in plant tissues of soybean cultivars infected by Corynespora cassiicola isolates. The experiment was conducted in a randomized block design in an 8 × 8 factorial arrangement, consisting of eight C. cassiicola isolates and eight soybean cultivars. Soybean plants were inoculated by spraying fungal suspension on the leaflets at a concentration of 2 × 104 conidia mL-1. Target spot lesions were classified, assigning scores for each type of symptom observed. The incidence of lesions in plant tissues was evaluated 10 days after inoculation, recording the presence of lesions. Five patterns of lesions were observed, ranging from small (0.74 mm) to large (9.30 mm) necrotic spots. Symptoms capable of causing defoliation occurred in BMX Elite IPRO, BRS 284, BMX Garra IPRO, and Nidera 5909 RG when inoculated with ISO 1S, ISO 4S, and ISO 11S. The highest frequency of lesioned trefoils was verified in the upper and lower (3rd and 1st trefoil) strata of soybean plants. Lesions were detected in cotyledons, unifoliate leaves, petioles, and stems of plants from all cultivars evaluated in this study. The isolate ISO 4S caused higher incidence of lesions in petioles compared with ISO 2A and ISO 2S. Moreover, ISO 4S produced more lesions in the stems of BMX Potência RR and BMX Force RR than BMX Elite IPRO. The incidence of petiole lesions caused by C. cassiicola increased as the petiole insertion height into the main stem decreased.
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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 | 0.000 | 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".