Soil erosion and phosphorus in runoff from agricultural cropland in Southwestern Ontario
Bibliographic record
Abstract
The Southwestern Ontario Soil and Water Environmental Enhancement Program (SWEEP) has been developed to respond to existing and potential deterioration of soil and water resources. This co-operative federal-provincial initiative has as its goals 1) The reduction of phosphorus loading to Lake Erie by 200 tons per year by 1990, from non-point agricultural cropland sources and 2) the increased productivity of the primary agricultural sector in southwestern Ontario by reducing or arresting soil erosion and degradation. Included in the SWEEP mandate is the evaluation of existing methods and development of new technologies which may be effective in addressing soil erosion and/or phosphorus loading problems. This report will attempt to facilitate this process by reviewing our current understanding of these problems as well as our ability to prescribe and implement solutions. Soil erosion and phosphorus loading will be examined with regard to physical processes, extent and distribution, perceived impacts and available remedies. In addition to documenting current knowledge and methods, informational deficiencies, which may impede the successful development of remedial measures, will be identified. It is hoped that identifying key issues and information gaps will help define research priorities and ensure that the financial and human resources of SWEEP are focused on areas of greatest need and potential. The desired end result of the research program will be the development and validation of technologies that are not only effective, but also acceptable to land managers in both their cost and complexity.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".