Deciphering inter-relationship between disease incidence and disease severity and prediction of yield loss in lentil-stemphylium pathosystem
Bibliographic record
Abstract
Exploring the relationship between disease incidence (DI) and disease severity (DS) of Stemphylium blight (SB) in lentil, the study aimed to assess the feasibility of using DI as a predictive measure for DS, reducing workload of disease quantification while ensuring accuracy. Quantifying disease is a critical task in field surveys, resistance breeding programs, and timely disease management strategies. Data on DI and DS of SB were collected from 35 different genotypes of lentil over three consecutive years, enabling the development of mathematical model which was validated using the widely cultivated lentil variety ‘Moitree’ (WBL 77) grown in West Bengal. The quadratic regression model, derived from independent datasets on DI and DS of genotypes, demonstrated a highly satisfactory fit, expressed as Y = −16.17 + 1.849X − 0.008X2; R2 = 0.95. This model showcased remarkable consistency across multiple growing seasons under varying disease pressures, confirming its reproducibility for other lentil varieties too. Critical point of DS (with cut-off value = 0.395) was also determined to take up timely crop protection measures. Yield loss estimations revealed for every unit increase in DS, an estimated 13.34 kg/ha yield loss could be expected, aligning well with observed field data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".