Non-point Source Pollution Control in the Great Lakes Region of North America:Experience and Enlightenment
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 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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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