Identifying New Persistent and Bioaccumulative Organics Among Chemicals in Commerce. III: Byproducts, Impurities, and Transformation Products
Bibliographic record
Abstract
The goal of this series of studies was to identify commercial chemicals that might be persistent and bioaccumulative (PB) and that were not being considered in current wastewater and aquatic environmental measurement programs. In this study, we focus on chemicals that are not on commercial chemical lists such as U.S. EPA's Inventory Update Rule but may be found as byproducts or impurities in commercial chemicals or are likely transformation products from commercial chemical use. We evaluated the 610 chemicals from our earlier publication as well as high production volume chemicals and identified 320 chemicals (39 byproducts and impurities, and 281 transformation products) that could be potential PB chemicals. Four examples are discussed in detail; these chemicals had a fair amount of information on the commercial synthesis and byproducts and impurities that might be found in the commercial product. Unfortunately for many of the 610 chemicals, as well as the transformation products, little or no information was available. Use of computer-aided software to predict the transformation pathways in combination with the biodegradation rules of thumb and some basic organic chemistry has allowed 281 potential PB transformation products to be suggested for some of the 610 commercial chemicals; more PB transformation products were not selected since microbial degradation often results in less persistent and less bioaccumulative metabolites.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".