Bibliographic record
Abstract
In this introductory paper it is suggested that multimedia mass balance models can play a valuable role in improving our understanding of the behavior of chemicals in the environment. They can provide a rational basis for chemical management by linking emission rates to prevailing environmental concentrations and identifying such issues of concern as tendency to bioaccumulate, persistence for excessive times, and the potential to undertake intermedia transport. Four principles are outlined: the need for iterative modeling starting with simple models and progressing toward more complexity with fidelity to real conditions; the value of evaluative models as a means of focusing attention on how the chemicals’ properties translate into fate; the need for more validation of these models by comparing observations with simulations; and finally, the value of the fugacity concept as a means of expressing multimedia partitioning, transport, and transformation more simply. These principles are demonstrated by a case study in which a variety of models is applied to assess the fate and transport of hexachlorobenzene in evaluative, regional, and aquatic environments. This example demonstrates that multimedia models can provide a comprehensive, quantitative picture of how specific chemicals behave in the environment, thus contributing to conditions engineered such that the beneficial uses of chemicals can be enjoyed sustainably and without risk of adverse effects on humans or the ecosystem.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".