Policy, actions and results: can meaningful nutrient reductions be achieved within the Minnesota and North Dakota portions of the Red River drainage basin?
Bibliographic record
Abstract
Numerous treaties and management strategies have been created in an attempt to either prevent or repair water-related problems and/or disputes involving the worlds 263 trans- boundary freshwater regimes. Canada and the U.S. are no different. However, modern times have not only revealed potentially new causes for trans-boundary water-related disputes, but weaknesses within the tools commonly used to address such disputes. Research was conducted using key groups and government departments within both Minnesota and North Dakota in an attempt to identify whether or not the economic, legal and social landscapes of the two states were favourable to reduce the nutrient loading to their portion of the Red River Drainage Basin which inevitably flows in Manitoba and enters Lake Winnipeg. The research revealed that; i) the difficulty of addressing NPS pollution, ii) a lack of cooperation from private landowners, iii) anti-government intervention, iv) a lack of funding for NPS related programs, v) uncertainties with the science, vi) negative economic impacts of implementing solutions, vii) interference by special interest groups and viii) legislative weaknesses of the Clean Water Act all create barriers that make achieving meaningful nutrient reductions unlikely. Due to these barriers, Minnesota’s and North Dakota’s hydro-hegemonic influence on nutrient levels within the Red River may aid in dictating potentially disastrous ecological conditions to Lake Winnipeg and place its long-term health in question.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".