Screening organic chemicals in commerce for emissions in the context of environmental and human exposure
Bibliographic record
Abstract
Quantitative knowledge of organic chemical release into the environment is essential to understand and predict human exposure as well as to develop rational control strategies for any substances of concern. While significant efforts have been invested to characterize and screen organic chemicals for hazardous properties, relatively less effort has been directed toward estimating emissions and hence also risks. Here, a rapid throughput method to estimate emissions of discrete organic chemicals in commerce has been developed, applied and evaluated to support screening studies aimed at ranking and identifying chemicals of potential concern. The method builds upon information in the European Union Technical Guidance Document and utilizes information on quantities in commerce (production and/or import rates), chemical function (use patterns) and physical-chemical properties to estimate emissions to air, soil and water within the OECD for five stages of the chemical life-cycle. The method is applied to 16,029 discrete substances (identified by CAS numbers) from five national and international high production volume lists. As access to consistent input data remains fragmented or even impossible, particular attention is given to estimating, evaluating and discussing uncertainties in the resulting emission scenarios. The uncertainty for individual substances typically spans 3 to 4 orders of magnitude for this initial tier screening method. Information on uncertainties in emissions is useful as any screening or categorization methods which solely rely on threshold values are at risk of leading to a significant number of either false positives or false negatives. A limited evaluation of the screening method's estimates for a sub-set of about 100 substances, compared against independent and more detailed emission scenarios presented in various European Risk Assessment Reports, highlights that up-to-date and accurate information on quantities in commerce as well as a detailed breakdown on chemical function are critically needed for developing more realistic emission scenarios.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".