Effects of Discharge Permit Trading on Water Quality Reliability
Bibliographic record
Abstract
Transferable discharge permit (TDP) programs show potential as cost-effective methods of pollution control. Nevertheless, there remain uncertainties that, if not adequately addressed, might impair their success. Concerns include modeling difficulties that might cause erroneous predictions of cost savings and environmental performance. This study focuses on environmental modeling associated with the stochastic environment. The mean-value first-order second-moment (MFOSM) method is extended to demonstrate how changes, due to discharge permit trading, in the environmental quality mean and∕or variance of a system will cause the environmental quality reliability of the system to either decrease, increase, or remain unchanged. The Willamette River in Oregon and the Athabasca River in Alberta, Canada, are used as example case studies. These river systems are simulated to predict how they might respond if trading were implemented. In this study, environmental quality reliability is defined as the probability of the minimum dissolved oxygen (DO) concentration over the length of the river meeting a predetermined standard. Generally, for the Willamette River, trading improves the reliability, while for the Athabasca River, trading makes the reliability worse.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".