Success of science-based best management practices in reducing swimming bans—a case study from Racine, Wisconsin, USA
Bibliographic record
Abstract
The Great Lakes region possesses over 10,000 miles of shoreline (US EPA and Government of Canada, 1995) which are home to over 1,000 beaches. These beaches represent a recreational outlet for over 30 million people (US EPA and Government of Canada, 1995) and yet many of them remain inaccessible for periods of time each bathing season due to water quality advisories. The reason for these advisories is often elusive to beach managers, hence impeding their ability to craft appropriate mitigation measures. Even when the sources of contamination are known, remediation measures may not be put into practice due to the perception that they are too costly. However, a recent study has demonstrated that investing in environmental improvements which increase the number of days available for swimming in the Great Lakes region by 20% would generate $2–$3 billion dollars in direct economic effects. Therefore, while beach closings and advisories continue to rise overall, some Great Lakes communities have recognized the potential for municipal beaches to generate revenue and increase the quality of life for their citizens and have undertaken comprehensive studies to improve recreational water quality. In Racine, Wisconsin, USA, research conducted to identify pollution sources guided the development of better beach management practices such as ecologically appropriate beach modifications, improved mechanical beach grooming strategies, and the redesign of a major storm water outlet (including installation of a constructed wetland area). Resulting improvements have reduced bathing water quality advisories from 66% of days during the swimming season in 2000 to 5% or less in four consecutive years (2005–2008). These improvements to Racine beaches facilitated Blue Wave certification from the Clean Beaches Council (Washington, DC); thereby restoring public confidence, increasing beach use by the residents and tourists, and expanding the role of the beachfront in the local economy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".