Standard Area Diagram Set for Bacterial Spot Quantification in Entire-Margined Leaves of Sour Passion Fruit
Bibliographic record
Abstract
This study developed and validated a standard area diagram set (SADs) to aid in the estimation of bacterial spot (Xanthomonas axonopodis pv. passiflorae) severity in entire-margined leaves of sour passion fruit (Passiflora edulis Sims). The SADs consisted of eight severity levels (3; 6; 12; 25; 50; 77, and 88%). For its validation, 20 raters, who initially estimated the disease severity without the aid of the SADs, were divided into groups (G1 and G3, inexperienced; G2 and G4, experienced). Subsequently, G1 and G2 performed the second evaluation without the SADs, and G3 and G4 completed the second evaluation using the proposed SADs. The accuracy and precision of the assessments were determined by simple linear regression and by the Lin’s concordance correlation coefficient. The increase in accuracy was confirmed by the 80% constant error-free estimates (G3 and G4) and 100% (G3) and 80% (G4) systematic error-free estimates when the SADs was used. Precision increased with the increase in the coefficient of determination, the reduction in absolute errors, and the increase in the reproducibility of the estimates between pairs of raters. Inexperienced raters benefited the most from the use of the SADs. The increase in the accuracy and precision in the non-aided groups, when present, was less pronounced than those increments observed in the SADs-aided groups. The Lin’s concordance correlation coefficient confirmed the increased accuracy and precision detected by the linear regression analysis and indicated increased agreement between the estimated and actual values of disease severity in the SADs-aided groups.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".