Rethinking TMDLs: Perspective Based on Community Survey
Bibliographic record
Abstract
The study investigated the perspectives of professionals involved in total maximum daily load (TMDL) development. A survey instrument was developed to understand the challenges and advancements necessary to enhance water quality management. This survey explored various dimensions of TMDL development, including identifying impaired waterbodies, water quality modeling, implementation, postimplementation assessment, and stakeholder engagement. Thirty-seven professionals involved in TMDL development took the survey. The results indicated a consensus on the need to reassess existing methodologies, particularly in the postimplementation phase, with a strong emphasis on the importance of sufficient funding for data collection. Limited resources, computational challenges, and a lack of trust in advanced models were identified as barriers to advancing water quality modeling. The participants also recognized the urgency of incorporating more validation data, especially through conventional monitoring and remote sensing, to enhance water quality modeling efforts. Although including social systems in modeling was considered crucial, it was not universally prioritized. This study developed a survey instrument to capture the evolving perspectives of stakeholders involved in TMDL processes. The survey’s structure provides a framework that can be improved and adapted for ongoing assessment and improvement. A continuous feedback mechanism, such as the one provided by the survey, is essential for ensuring that TMDL practices remain responsive to emerging challenges and are better equipped to manage water quality in an increasingly complex landscape.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".