The role of persistence in chemical evaluations
Bibliographic record
Abstract
Abstract The initial stage in the assessment and priority setting of chemicals for their potential to cause harm to humans and the environment is usually a hazard assessment employing metrics for persistence, bioaccumulation, and inherent toxicity. This hazard assessment is followed, when necessary, by the more demanding task of risk assessment. Hazard assessment of data and processes influencing persistence are discussed, leading to a number of suggestions for more effective evaluation. These include 1) an initial focus on accurate data for intensive chemical partitioning and reaction half-life properties that are universally applicable as distinct from extensive properties that can be included later on a location-specific basis; 2) separate treatments of near-field and far-field exposures; 3) a focus on persistence and its effect on levels of exposure, especially for substances for which “time to exposure” is less than “time to degradation” and have been termed “pseudo-persistent.” We show that “continuously present” is a better descriptor of this concern. Case studies illustrate and support these suggestions. Data on the intensive properties and on exposure pathways are best combined in evaluative multimedia mass balance models that can provide a clear depiction of the likely chemical fate, exposure routes, and levels. The information generated by the mass balance models can serve to justify and direct a full risk assessment that includes region-specific information on chemical quantities, estimates of exposure, and potential for adverse effects. Integr Environ Assess Manag 2014;10:588–594. © 2014 SETAC Key Points We demonstrate the importance of persistence as a criterion for hazard assessment of chemicals. We show the merit of addressing intensive chemical properties before extensive properties, We show the need to address near-field and far-field exposures separately. We introduce the concept of "time to exposure" and use it to show that continuously present is a term preferable to pseudo-persistent. These key points are illustrated using hypothetical case studies involving simple mass balance calculations.
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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 | 0.047 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".