Bibliographic record
Abstract
A general review is presented of the roles of QSARs and mass balance models as tools for assessing the environmental fate and effects of chemicals of commerce. It is argued that all such chemicals must be assessed using a consistent and transparent methodology that uses chemical property data derived from QSARs, or experimental determinations when possible and applies evaluative or region-specific environmental models. These data and models enable an assessment to be made of the key chemical features of persistence, bioaccumulation, potential for long-range transport and toxicity. The other key feature is quantity used or discharged to the environment. A taxonomy of environmental models is presented in which it is suggested that rather than develop a single comprehensive model, the aim should be to establish a set of coordinated and consistent models treating evaluative and real environmental systems at a variety of scales from local to global and including food web models, organism-specific models and human exposure and pharmacokinetic models. The concentrations derived from these models can then be compared with levels judged to be of toxic significance. A brief account is given of perceived QSAR needs in terms of partitioning, reactivity, transport and toxicity data to support these models.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".