Standard Area Diagram Set for Scab Evaluation in Fruits of sour Passion Fruit
Bibliographic record
Abstract
Scab (Cladosporium spp.) significantly comprises the commercial acceptance of sour passion fruit (Passiflora edulis) because of the deformed and atrophied fruit appearance resulting from the development of the lesions. Therefore, the objective of this study was to elaborate and validate a standard area diagram set (SADs) for the severity evaluation of scab in fruits of sour passion fruit. The SADs comprised eight severity levels (0.6; 1; 2; 4; 8; 16; 37; and 46%) and was validated by 20 raters (G1 and G3, inexperienced; G2 and G4, experienced). Initially, all raters performed a non-aided SADs evaluation of the scab severity. Afterward, G1 and G2 completed the second evaluation without the proposed SADs, whereas G3 and G4 performed a SADs-aided assessment of the disease severity. The accuracy and precision of the evaluations were determined by simple linear regression and by the Lin’s concordance correlation coefficient. Constant and systematic errors decreased with the use of the SADs, demonstrating an approximation between the estimated and the actual values. Precision increased with an increase in the coefficient of determination. Also, the absolute error reduced by 66% (G3) and 47% (G4). Therefore, 94.4% (G3) and 98.8% (G4) of the estimates had up to ±10% of errors, which corresponds to a 20.4% (G3) and 5.6% (G4) increment in the estimates with errors within this variation range. As a result, accuracy and precision were higher in the SADs-aided groups. Inexperienced raters were the most benefited by the use of the SADs. The accuracy and precision of the non-aided groups had a slight or no increase when compared with 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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".