Deterministic and probabilistic approaches to the development of pH total maximum daily loads: a comparative analysis
Bibliographic record
Abstract
The most commonly used deterministic approach to the development of total maximum daily loads (TMDLs) fails to explicitly address issues related to a margin of safety and inherent variability of streamflows in the process of TMDL development. In this paper, the deterministic approach to pH TMDL development for Beech Creek watershed, Muhlenberg County, Kentucky, proposed by Ormsbee, Elshorbagy and Zechman is discussed. The shortcomings and the limitations of the assumptions associated with the deterministic approach are highlighted. An alternative probabilistic approach, to cope with the percentile-based water quality standards based on Monte Carlo simulation, is presented and compared to the deterministic approach. The proposed probabilistic approach provides a deeper insight into the issue of uncertainty and emphasizes the importance of handling the water quality standards and TMDLs in terms of magnitude and frequency rather than a single-valued approach. Expected exceedances and the confidence of compliance with percentile-based standards are estimated. Accordingly, an objective method of estimating the margin of safety for pH TMDLs is proposed.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".