An updated Quantitative Water Air Sediment Interaction (QWASI) model for evaluating chemical fate and input parameter sensitivities in aquatic systems: Application to D5 (decamethylcyclopentasiloxane) and PCB-180 in two lakes
Bibliographic record
Abstract
The QWASI fugacity mass balance model has been widely used since 1983 for both scientific and regulatory purposes to estimate the concentrations of organic chemicals in water and sediment, given an assumed rate of chemical emission, advective inflow in water or deposition from the atmosphere. It has become apparent that an updated version is required, especially to incorporate improved methods of obtaining input parameters such as partition coefficients. Accordingly, the model has been revised and it is now available in spreadsheet format. Changes to the model are described and the new version is applied to two chemicals, D5 (decamethylcyclopentasiloxane) and PCB-180, in two lakes, Lake Pepin (MN, USA) and Lake Ontario, showing the model's capability of illustrating both the chemical to chemical differences and lake to lake differences. Since there are now increased regulatory demands for rigorous sensitivity and uncertainty analyses, these aspects are discussed and two approaches are illustrated. It is concluded that the new QWASI water quality model can be of value for both evaluative and simulation purposes, thus providing a tool for obtaining an improved understanding of chemical mass balances in lakes, as a contribution to the assessment of fate and exposure and as a step towards the assessment of risk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".