Continued participation in an asbestos fiber-counting proficiency test with relocatable grid slides
Notice bibliographique
Résumé
The effect of using relocatable reference slides of chrysotile and amosite in asbestos fiber counting proficiency testing was examined for volunteer analysts from laboratories in the USA. Results of participation in one round have been published; two more rounds are reported here. In the first round, participants were asked to draw what they saw, allowing identification of error type by comparison to the reference. In later rounds only the number of fibers per field was reported since the number of errors per field has been shown to be a reasonable estimate of proficiency. The third round included a training exercise. The total number of participants stayed reasonably constant with some reduction over time. More restricted numbers participated from round to round. Those who dropped out had lower average scores than those that remained in the program; from 2006 to 2007 this difference was significant, but for 2007 to 2008 it was not. The overall results for amosite were generally good compared to an arbitrary proficiency score of 60, and continued to improve further over time. The results for chrysotile were better in rounds 1 and 3 than round 2, so that both attention to detail (drawing the fibers in round 1) and training (round 3) may improve performance, which is consistent with the major type of error being oversight of fine fibers. However, the results are still poor, even by round 3, and no analyst achieved a score of 60 in all three rounds. Further improvement is preferred since chrysotile is the most commonly encountered type of asbestos in the USA. Depending on the adopted score for proficiency many laboratories or analysts may be labeled as poor performers and this may be a deterrent to voluntary participation in this type of exercise, especially for those in most need of assistance. Participants have tested new relocatable reference asbestos proficiency counting slides in three rounds of chrysotile and three rounds of amosite. Performance for amosite was good. Poor performance for chrysotile appears to be improved by greater attention and training.
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 | 0,001 |
| 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 ».