Towards Consistent Evaluation of the Persistence of Organic, Inorganic and Metallic Substances
Bibliographic record
Abstract
Several criteria, including persistence (P), bioaccumulation (B), its related factor bioavailability, toxicity (T), and potential for long range transport (LRT) are currently applied when assessing the environmental hazard and risk associated with the use of chemicals of commerce. Whereas information on B and T criteria may be obtained by experimental measurement using standard tests or by the use of mass balance models, in the case of P and LRT no such standard tests exist, except for certain degradation rates in single media. Nor can these properties be measured in the environment at large. Here we focus on the criterion of persistence and its evaluation using steady-state multimedia mass balance models. It is concluded that it is possible to assess the persistence of all chemical substances including organics, inorganics, and metals using a common methodology in which mass balance models are applied to describe the substance's behavior in a specified environment or “unit world.” This avoids inconsistent evaluation and excessive regulatory fragmentation, which is likely if assessment procedures are specific to classes of chemicals. It is essential that persistence be recognized as only one of several factors influencing hazard and risk. Regulatory actions must also thus reflect other attributes such as toxicity, bioaccumulation, quantities used, and the societal value of the substances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".