Quantifying the intrarater repeatability and interrater reliability of visual and remote-sensing disease-assessment methods in the alfalfa foliar pathosystem
Bibliographic record
Abstract
The precision of visual and remote-sensing assessment methods for the alfalfa foliar pathosystems was studied. Precision was defined as the intrarater repeatability and interrater reliability of a disease-assessment method. Visual disease assessments were performed on 10 alfalfa stems per plot by four raters for disease incidence, disease severity, and percent defoliation. Remote-sensing assessments were also performed by the same four raters, using a hand-held, multispectral radiometer to measure the percentage of sunlight reflected from alfalfa canopies. The F statistics, intercepts, slopes, coefficients of determination (R 2), standard errors of the estimate for y, and coefficients of variation were used to quantify and compare the repeatabilities and reliabilities of each assessment method. Among the three visual disease-assessment methods evaluated, percent defoliation had the highest intrarater repeatability and interrater reliability with R 2 ranging from 0.89 to 0.97, whereas R 2 ranged from 0.15 to 0.95 among the four raters for disease incidence and disease severity. For intrarater repeatabilities and interrater reliabilities with the remote-sensing assessment method, R 2 ranged from 0.87 to 0.99. The standard errors of the estimate for y and coefficients of variation values for intrarater repeatabilities and interrater reliabilities, when using the remote-sensing assessment method, were approximately one half of the values for the percent-defoliation assessment method. In summary, percent defoliation had the best precision among the three visual assessment methods, and the percent-reflectance assessment method had the highest precision compared with all three visual assessment methods.
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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 | 0.052 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".