Consumer Exposure Scenarios in the Health Canada Existing Substances Program
Notice bibliographique
Résumé
TS1-13 Abstract: Canada is the first country to introduce a legislative requirement for systematic priority setting for all existing chemicals. In addition to a continuing mandate to establish and conduct full assessments for lists of priority substances, the Canadian Environmental Protection Act (CEPA '99) requires that the Ministers of Health and Environment complete “categorization” (priority setting) of all of the approximately 23,000 substances on the Domestic Substances List (DSL) by September 2006, with subsequent screening and full risk assessment, when warranted. These requirements set the stage for identification of highest priority substances for subsequent introduction of control measures to reduce exposure in both consumer products and the general environment. This precedent setting mandate has required the development and refinement of methodology for priority setting and risk assessment for a wide range of diverse substances. These approaches draw maximally from available, often generic information as a basis to consider large numbers of substances, for which individual data on exposure and hazard are often limited. Estimation of exposure to consumer products is addressed both in priority setting and assessment stages of the program. For consumer products, an approach has been developed to provide quantitative estimates of exposure relevant in a priority setting context. This has required consideration of the relative degree of conservatism in existing exposure modeling algorithms and development of a considerable number of additional scenarios and leads to quantitative plausible maximum estimates of exposure of individuals in the general population by age group based on use scenario, physical/chemical properties, and bioavailability. Comparison of the output with measures of exposure–response for relevant critical effects leads to substances being set aside from further consideration or prioritized for additional assessment. After 2006, the approach will also contribute to efficient screening, delineating the focus of subsequent assessment. The development and integration of consumer exposure modeling in increasingly broad legislative mandates to systematically consider all existing chemicals raises a number of issues relevant to exposure assessment in differing jurisdictions. These include transparency, consistency, usability, and defensibility of the models, including relevant degree of complexity for priority setting versus assessment. These issues are discussed through examples and lessons learned from the development of the approach to consumer products for priority setting and screening assessment.
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 | 0,001 | 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 ».