Implementation of Low Impact Development in Orange County, California: Challenges and Solutions in Implementation and Assessment of Progress
Bibliographic record
Abstract
This paper discusses Orange County's transition from a conventional urban development approach that resulted in significant damages to natural stream morphology, habitat, and water quality to a more balanced approach that attempts to balance predevelopment and post-development hydrology to protect these resources.It then summarizes the framework and innovative approaches being undertaken to implement and assess the Low Impact Development (LID) in Orange County.Participating in the regional study to understand the effectiveness of LID, Orange County has developed guidance documents and created a systematic framework to assess the LID performance.These are aimed to standardize the LID approaches and develop alternative compliance program through water quality credit trading in the LID-restricted areas.Through two case studies, Glassell Campus Stormwater LID Retrofit Project and Legacy Campus Water Quality Credit Trading Program, we find out key factors to the success of LID implementation include: the site survey, modeling, and monitoring prior to the project design; the post-project standard monitoring and data sharing; and the flexible water quality credit trading system.China can potentially learn from Orange County's experiences for its implementation and assessment of LID.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".