Using Partnerships and Community Science to Protect Wild and Scenic Rivers in the Eastern United States
Bibliographic record
Abstract
The Musconetcong (New Jersey) and the Sudbury-Assabet-Concord (Massachusetts) are federally-designated Partnership Wild and Scenic Rivers, a model for river conservation under the Wild and Scenic Rivers Act. These two rivers are embedded in a patchwork of private and public land ownership. The Act has been used to facilitate partnerships among municipal, state, federal and local non-profit actors to implement river conservation plans. These partnerships have supported community science-based monitoring to make the case for dam removal and stricter water pollution controls. Two case studies examine using community science to provide actionable data to decision-makers. In New Jersey, a documented increase in macroinvertebrates post-dam removal supported additional dam removals, leading to the return of American shad to the river. Quality controls and training proved to be key components. In Massachusetts, stricter effluent discharge permits reduced instream Total Phosphorus from 0.8 mg/L in 1999 to the eutrophication threshold of 0.023–0.05 mg/L. Community engagement in river science and stewardship was an important co-benefit. As many US rivers evolve from generating hydropower and conveying waste into major recreational resources, local organizations are uniquely positioned to engage the public and generate quality-controlled data to use in advocating for major improvements in water and habitat quality. Useful policy and regulatory frameworks for broader applicability are suggested.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| 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".