Future soil erosion trends in Canadian agricultural lands from runoff and sustainability impacts
Bibliographic record
Abstract
Human activities have significantly altered agricultural regions, leading to critical issues such as reduced soil fertility, biodiversity loss, and accelerated soil erosion. Despite their importance, reliable erosion maps for Canadian croplands remain scarce, hindering effective mitigation strategies. Here, we aimed to map erosion-prone areas in Canada by combining remote sensing and artificial intelligence methods under current and future climate scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our results revealed that, on a national average, soil erosion in Canada ranges from 4.72 to 6.64 t/ha/yr. All the scenarios indicate an increase in soil erosion over time. Soil degradation could become a more severe problem in the future. Our findings revealed that by 2030, 81,038 km² of agricultural land will experience high and severe erosion risks, indicating a significant 53.9% increase compared with that in 2020. The development of accurate soil erosion risk maps will not only enhance targeted conservation efforts but also serve as a critical tool for policymakers to implement effective soil management strategies, contributing to sustainable agriculture and climate resilience at a broader scale.
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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 | 0.001 |
| 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 | 0.000 |
| 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".