Non-point Source Pollution Control in the Great Lakes Region of North America:Experience and Enlightenment
Bibliographic record
Abstract
The Great Lakes Region of North America, covering an area of 2.44×105 km2 and having a water storage of 2.3×105 km3, which consists of Lakes Superior, Michigan, Huron, Erie and Ontario, is the largest freshwater lakes on the earth and accounts for about 18% of its total freshwater resources. The pollution sources of the Lakes include soil runoffs, agrochemical matters, urban waste materials, emissions of industrial districts and the exudates from solid waste landfill. They are also influenced by pollutants of atmospheric sedimentation, such as snow, rainfall and dust. Non-point source(NPS) pollution is a serious problem world-wide leading to biological habitat changes and biodiversity reduction and affecting human health. For controlling NPS pollution, the US government has taken a series of national actions, including EPA, NOAA, USDA and USGS plans and President's Water Quality Initiative, to enlarge civic participation consciousness. By analysing the experience of controlling NPS pollution in the Great Lakes Region of North America, we can get two enlightenments, i.e. initiating research on mechanisms and integrated control technique of agricultural NPS pollution in the Reservoir Area of the Three Gorges as soon as possible, and working out action plans for controlling NPS pollution at the national, regional and departmental levels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".