Exploring REACH as a potential data source for characterizing ecotoxicity in life cycle assessment
Bibliographic record
Abstract
Toxicity models in life cycle impact assessment (LCIA) currently only characterize a small fraction of marketed substances, mostly because of limitations in the underlying ecotoxicity data. One approach to improve the current data situation in LCIA is to identify new data sources, such as the European Registration, Evaluation, Authorisation, and Restriction of Chemicals (REACH) database. The present study explored REACH as a potential data source for LCIA based on matching reported ecotoxicity data for substances that are currently also included in the United Nations Environment Programme/Society for Environmental Toxicology and Chemistry (UNEP/SETAC) scientific consensus model USEtox for characterizing toxicity impacts. Data are evaluated with respect to number of data points, reported reliability, and test duration, and are compared with data listed in USEtox at the level of hazardous concentration for 50% of the covered species per substance. The results emphasize differences between data available via REACH and in USEtox. The comparison of ecotoxicity data from REACH and USEtox shows potential for using REACH ecotoxicity data in LCIA toxicity characterization, but also highlights issues related to compliance of submitted data with REACH requirements as well as different assumptions underlying regulatory risk assessment under REACH versus data needed for LCIA. Thus, further research is required to address data quality, pre-processing, and applicability, before considering data submitted under REACH as a data source for use in LCIA, and also to explore additionally available data sources, published studies, and reports. Environ Toxicol Chem 2017;36:492-500. © 2016 SETAC.
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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 | 0.041 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".