Deliverable 5.2 Supplementary Data - Circular Economy, Environmental, and Emissions database for the Global Chemical Inventory
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.394 | 0.294 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".