Development of a risk-based TMDL assessment approach using the integrated modeling system GIBSI
Bibliographic record
Abstract
Using the integrated modeling system GIB SI and a case study, this paper presents the development of a risk-based TMDL assessment approach that links wet (nonpoint/diffuse) and dry weather (point) sources to a probability of exceeding water quality standards (WQS) governing wateruses. The case study focused on determining whether WQS defining recreational uses of water requiring direct and prolonged contact were attainable if the waste water effluent of a small town was treated using aerated lagoons and if the agricultural nonpoint source (NPS) loads were reduced using different fertilization rates. Dry weather sources were assumed to solely contribute to bacteriological impairment of the studied river reach. Meanwhile, both wet and dry weather sources were assumed to contribute to aesthetic impairment. Simulation results showed that treating the waste water effluent while reducing the agricultural NPS loads by 27% allowed on average over a four-year study period for attainment of the bacteriological WQS 100% of the summer time while lowering the probability of exceeding the aesthetic WQS from 0.32 to 0.19 (30 to 18 days). The results of this study showed this risk-based assessment approach was well suited to establish TMDL. These probabilities should be evaluated using long meteorological series.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".