Five-Stage Environmental Exposure Assessment Strategy for Mixtures: Gasoline as a Case Study
Bibliographic record
Abstract
A five-stage strategy is suggested for conducting an exposure assessment of mixtures that may contain numerous chemical components. The stages are: (1) determination of mixture composition and variability, (2) selection of component groups within the mixture and documentation of criteria used for this selection, (3) compilation of relevant property data for each group, (4) assessment of environmental fate of each group, and (5) assessment of environmental and human exposure to each group and to the mixture as a whole. A subsequent step is the assessment of environmental and/or human risk associated with the individual and aggregate exposure to each group. The approach is illustrated by application to gasoline, which is treated as 24 component groups or hydrocarbon blocks. Focusing on stages 2-4, the illustration shows that the groups display widely different environmental fates as a result of their different physicochemical properties, degradation half-lives, and mode-of-entry into the environment. As a result, the relative proportions of groups in each environmental medium (such as air and water) differ greatly from that of the original mixture. It is thus important to treat gasoline and similar mixtures as a number of component groups instead of as a single substance. A generic procedure is suggested in which the model is run for unit emissions of each component group to air, water, and soil. These results are compiled into matrices that can then be conveniently scaled to actual emission rates without rerunning the model. Methods for determining subsequent exposure and risk are also briefly outlined.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".