The quality of fiber counts using improved slides with relocatable fields
Notice bibliographique
Résumé
A parameter based on discrepancies between reported fibers and verified fibers of relocatable slides is shown to be effective in monitoring the quality of airborne fiber counts. Analysts report only the fibers in each field examined. The verified fibers were determined by two experienced analysts, and are here considered as a "true" value. Most of the verified fibers were confirmed by the reported fibers, and the disputed fibers or fiber counting errors were all located and accounted for. In this study, reference (REF) slides were manufactured from proficiency analytical test (PAT) filter samples from the American Industrial Hygiene Association containing chrysotile or amosite. The slides were made using coverglasses bearing a grid pattern to allow accurate re-examinations. These coverglasses are an improved version of those used in previous studies. Seventy-four out of 85 amosite results and 51 out of 60 chrysotile results of REF slides were within their PAT proficiency ranges. When all reported fibers were normalized against their respective verified fibers, the average fiber count was over-estimated for amosite by 38.3% and under-estimated for chrysotile by 30.4%. The error from counting short fibers (sizing-extra) was 82.6% of the extra fibers and accounted for the 38% over-estimation of amosite fibers. For chrysotile fibers, sizing-extra errors were 74.0% of the extra fibers, but by far the larger errors were oversight-missing errors, which were 96.7% of the missing fibers and accounted for the 30% under-estimation of the chrysotile fibers. The discrepancies were found to be linearly related to counting errors as had been noted in a previous study, giving further weight to a proposed score, calculated from the discrepancy parameter (SigmaD(+) + |SigmaD(-)|)/VF(total), for evaluating the proficiencies of analysts. If a proficiency score =60 is selected, 48 out of 85 amosite results and 17 out of 60 chrysotile results satisfied this criterion in this study. The number of fiber counting errors in this study was larger than could be expected by PAT proficiency criteria. It may be useful to complement existing proficiency test programs with these REF slides. At the end of each proficiency testing round, detailed reports of discrepancies can be provided to participants so that they can improve on their skills in searching and sizing fibers and minimize their counting errors. In addition, the internal quality control program of each laboratory could include counting REF slides regularly by all analysts with control charts of (SigmaD(+)/VF(total)), (SigmaD(-)/VF(total)), (SigmaD(+) + |SigmaD(-)|)/VF(total) and RF(total)/VF(total) maintained to monitor errors, proficiencies and intercounter variations. Ten percent of relocatable slides of routine samples could also be recounted to monitor intracounter variation.
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 ».