Deliverable 5.2 Supplementary Data - Circular Economy, Environmental, and Emissions database for the Global Chemical Inventory
Notice bibliographique
Résumé
This is a dataset related to the Deliverable 5.2 if ZeroPM. The goal of Delivearble 5.2 is to categorize as many chemicals in the GCI as possible into categories of emission to aquatic environments, namely “emissions confirmed,” “emissions likely,” and “no emission data available.” This is part of an effort to see which persistent and mobile substances and substance groups should be prioritized for future attention. The data sources for this deliverable include the following inventories for specific chemical uses and inventories of chemical monitoring data: · The chemical inventories for specific uses include databases from Canada, the US, the EU, and China, each contributing to the identification of chemicals likely to result in emissions. These inventories cover pesticides (PPID, APPRIL, EU Pesticide Active Substances), cosmetics (CosIng, IECIC), and biocides (EU Biocidal Active Substances). Collectively, thousands of chemicals from these sources—ranging from 4 in IECIC to over 2,500 in CosIng—are also found in the Global Chemical Inventory. Despite some substances potentially being inactive, all are categorized as “emissions likely” due to their intended applications and regulatory contexts. · The inventories of chemical monitoring data include four major datasets that provide evidence of chemical presence in various environmental media.The LitChemPlast database compiles data from 372 studies on chemicals analytically detected in plastics, identifying 1,432 substances also present in the Global Chemical Inventory and categorized as “emissions likely” due to the abundance of (micro)plastic pollution in aquatic resources. Arp and Hale (2022) reviewed 55 studies on organic chemicals in aquatic environments, identifying 873 overlapping substances, which are classified as “emissions confirmed.” Two data sets external to ZeroPM are the Multimedia Monitoring Database (MMDB), which aggregates over 63 million records from multiple sources, with 1,398 chemicals matching the Global Chemical Inventory for substances monitored in the aquatic environment and therefore were considered “emissions confirmed.” Lastly, the ANST/POL list from Muir et al. (2023) includes 8,112 chemicals detected in environmental media over 50 years, categorized as “emissions likely”, as it was unclear if they were reported in the aquatic environment or another environmental media. By compiling all datasets, a total of 10,556 substances from the Global Chemical Inventory were assigned emission categories to aquatic environments. 7,352 substances appeared in only one dataset, while 3,335 were found in multiple datasets, highlighting overlaps and reinforcing the value of integrating diverse sources. Specifically, 1,749 substances were classified as “emissions confirmed” based on direct environmental measurements, and 8,938 as “emissions likely” due to their intended uses or presence in relevant inventories. This distribution underscores the importance of using multiple datasets to ensure comprehensive and robust chemical emission assessments.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,005 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,394 | 0,294 |
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 source (Gemma direct ou Codex distillé), 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 ».