Development of a Quantitative Approach to Incorporating Stakeholder Values into Total Maximum Daily Loads: Dominquez Channel Case Study
Bibliographic record
Abstract
The federal Clean Water Act (CWA) Section 303(d)(1)(A) requires each state to conduct a biennial assessment of its waters, and identify those waters that are not achieving water quality standards. The result of this assessment is called the 303(d) list. The CWA also requires states to establish a priority ranking for waters on the 303(d) list of impaired waters and to develop and implement Total Maximum Daily Loads (TMDLs) for these waters. Over 30,000 segments of waterways have been listed as impaired by the Environmental Protection Agency (EPA). The EPA has requested local communities to submit plans to reduce discharges by specified dates or have them developed by the EPA. An investigation of this process found that plans to reduce discharges were being developed based on a wide range of site investigation methods. The Department of Energy requested Lawrence Livermore National Laboratory to develop appropriate tools to assist in improving the TMDL process. The EPA has shown support and encouragement of this effort. Our investigation found that improving the stakeholder input process would facilitate many of the TMDL processes, given the resources available to the interested and responsible parties. The first model that we have developed is a stakeholder allocation model (SAM). The SAM uses multi-attribute utility theory to quantitatively structure the preferences of the major stakeholder groups, and develop both individual stakeholder group utility functions and overall stakeholder utility function for a watershed. The test site we selected was the Dominquez Channel watershed in Los Angeles, California. The major stakeholder groups interviewed were (1) nongovernmental organizations, (2) oil refineries, (3) the Port of Los Angeles, and (4) the Los Angeles Department of Public Works. The decision-maker that will determine the acceptable utility values is the Los Angeles Regional Water Quality Board. The preliminary results have shown some different values among stakeholders, especially in the areas of scheduling and cost of the implementation plan. However, the attribute list has also identified the value or importance of each area, giving the decision-maker the ability to make tradeoffs to maximize the groups overall utility. Final decisions are not disclosed in this paper due to ongoing negotiations by the stakeholders.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".