Disease Severity Estimates—Effects of Rater Accuracy and Assessment Methods for Comparing Treatments
Notice bibliographique
Résumé
Assessment of disease severity is required for several purposes in plant pathology; most often, the estimates are made visually. It is established that visual estimates can be inaccurate and unreliable. The ramifications of biased or imprecise estimates by raters have not been fully explored using empirical data, partly because of the logistical difficulties involved in different raters assessing the same leaves for which actual disease has been measured in a replicated experiment with multiple treatments. In this study, nearest percent estimates (NPEs) of Septoria leaf blotch (SLB) on leaves of winter wheat from nontreated and fungicide-treated plots were assessed in both 2006 and 2007 by four raters and compared with assumed actual values measured using image analysis. Lin’s concordance correlation (LCC, ρ c ) was used to assess agreement between the two approaches. NPEs were converted to Horsfall-Barratt (HB) midpoints and were compared with actual values. The estimates of SLB severity from fungicide-treated and nontreated plots were analyzed using generalized linear mixed modeling to ascertain effects of rater using both the NPE and HB values. Rater 1 showed good accuracy (ρ c = 0.986 to 0.999), while raters 3 and 4 were less accurate (ρ c = 0.205 to 0.936). Conversion to the HB scale had little effect on bias but reduced numerically both precision and accuracy for most raters on most assessment dates (precision, r = −0.001 to −0.132; and accuracy, ρ c = −0.003 to −0.468). Interrater reliability was also reduced slightly by conversion of estimates to HB midpoint values. Estimates of mean SLB severity were significantly different between image analysis and raters 2, 3, and 4, and there were frequently significant differences among raters (F = 151 to 1,260, P = 0.001 to P < 0.0001). Only on 26 June 2007 did conversion to the HB scale change the means separation ranking of rater estimates. Nonetheless, image analysis and all raters were able to differentiate control and treated-plot treatments (F = 116 to 1,952, P = 0.002 to P < 0.0001, depending on date and rater). Conversion of NPEs to the HB scale tended to reduce F values slightly (2006: NPEs, F = 116 to 276, P = 0.002 to 0.0005; and, for the HB-converted values, F = 101 to 270, P = 0.002 to 0.0005; 2007: NPEs, F = 164 to 1,952, P = 0.001 to P < 0.0001; and, for HB-converted values, F = 126 to 1,633, P = 0.002 to P < 0.0001). The results reaffirm the need for accurate and reliable disease assessment to minimize over- or underestimates compared with actual disease, and the data we present support the view that, where multiple raters are deployed, they should be assigned in a manner to reduce any potential effect of rater differences on the analysis.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».