Success of science-based best management practices in reducing swimming bans—a case study from Racine, Wisconsin, USA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it