Prediction of soil salinity risk by digital terrain modeling in the Canadian prairies
Bibliographic record
Abstract
Soil salinisation is a typical problem for the Canadian prairies. At macro-topographic scale, build-up of salts occurs in depressions. However, this relationship is not displayed on existing small-scale maps of soil salinity. To improve these maps, one can use a concept of accumulation, transition and dissipation zones of the landsurface. The concept allows one to reveal depressions (topographically expressed accumulation zones) using digital models of horizontal and vertical curvatures, or accumulation and mean curvatures derived from a digital elevation model. We applied the concept of accumulation, transition and dissipation zones to improve an existing small-scale map of the salinity risk index for the prairies and adjacent areas. A comparison of the old and the improved maps demonstrated that once data on depressions have been taken into account, areas marked by salinity risk decreased significantly. We suggest that the method used may prevent an overestimation in predictions of soil cover degradation due to salinisation. The method used can also reveal saline areas linked with discharges of saline aquifers. This is because sites marked by high discharges of groundwater usually relate to sites of intensive fracturing of geological materials, which are closely associated with topographically expressed accumulation zones. Key words: Digital terrain models, topography, soil salinisation, mapping
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".