Development of a Tool for Systematic Integration of Traditional and New Approach Methods for Prioritizing Chemical Lists
Bibliographic record
Abstract
Multiple regulatory bodies (EPA, ECHA, Health Canada) are currently tasked with prioritizing chemicals for data collection and risk assessments. These prioritization efforts are in response to regulatory mandates to identify chemicals for further assessment. We have developed a web-based application that enables a rapid, flexible and transparent prioritization process. The tool includes multiple data streams related to human and ecological hazard, exposure, and physicochemical properties (persistence and bioaccumulation). For human hazard, the data streams include quantitative points of departure (PODs) that are compiled from multiple sources such as EPA ToxRefDB, ECHA, COSMOS; estimated PODs from high-throughput in vitro screening assays and computational models; and qualitative measurements and predictions of specific endpoints (e.g., genotoxicity, endocrine activity). For ecological hazard, quantitative PODs are taken from the EPA ECOTOX database. Exposure information includes production volume, quantitative predictions using the EPA ExpoCast and SHEDS models, biomonitoring data, and qualitative information such as media occurrence, use profiles and likelihood of consumer and childhood exposures. The use of the tool is illustrated by prioritizing chemicals related to TSCA and the Safer Choice Ingredient List. The underpinning data streams for this application are already available in the EPA CompTox Chemistry Dashboard and have been repurposed to deliver this application. This is in keeping with our overarching software development methodology of providing multiple “building blocks” in the form of databases, web services and visualization components to deliver fit-for purpose applications to the relevant audiences. This abstract does not necessarily represent U.S. EPA policy.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.016 |
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".