The impacts of land use on the risk of soil erosion on agricultural land in Canada.
Bibliographic record
Abstract
Using established erosion models and national databases, A Soil Erosion Risk Indicator (SoilERI) was developed under the National Agri-Environmental Health Analysis and Reporting Program (NAHARP) in Canada to assess the risk of soil erosion in agricultural land from the combined effects of tillage, water and wind erosion processes. The indicator was build upon the Soil Landscape of Canada (SLC) polygon and was then aggregated to the province and national scale. It reflects the characteristics of the climate, soil and topography and responds to changes in land use over the 25-year period between 1981 and 2006. The results showed that the risk of soil erosion on Canadian cropland has steadily declined with time since the 1980s, largely due to the adoption of the conservation tillage, particularly no-till systems. However, there are still areas in every province with risks of unsustainable soil erosion. The risk of soil erosion was greatest under potato and sugar beet production and corn and soybean produced with conventional tillage. Serious erosion occurs on an important portion of cropland in southern Ontario and in Atlantic Canada. The information obtained in this study could help the decision makers to better target the hot spot of soil erosion in different scales and to design the best management practices for a given region.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".