Development of a method for estimating the likelihood of crack flow in Canadian agricultural soils at the landscape scale
Bibliographic record
Abstract
Indicators of risk of water contamination by agricultural pollutants are developed in Canada to assess sustainability of agriculture. Crack flow (CF), a key pathway for sub-surface contaminant transport, is part of the transport-hydrology algorithm used in two of these risk indicators. The objective was to develop a methodology for predicting the likelihood of CF in Canadian agricultural soils at the landscape scale. The algorithm considers soil clay content, crack development followed by a runoff event based on water budget, tile drainage, and crops. More than 40% of Canadian farmlands had moderate to very high likelihood of CF, mainly in Manitoba, Ontario and Quebec, due to frequent runoffs on cracked clay soils potentially contributing to groundwater contamination. In Ontario and Quebec, farmlands with high CF likelihood correspond to regions under intensive tile drainage, which increases the risk of lateral translocation of contaminants to surface water bodies. Besides being a component of risk indicators of water contamination by phosphorus and coliforms, the CF algorithm and maps can be used to identify areas at risk of subsurface water contamination. Best management practices, adapted to reduce CF can then be targeted to these areas.Key words: Agrichemicals, contaminant transport, macropore flow, preferential flow, risk assessment, risk indicators
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".