www.elsevier.com/locate/envsoft Prediction of soil properties by digital terrain modelling
Bibliographic record
Abstract
We investigated two approaches for large-scale analysis and prediction of the spatial distribution of soil properties in an agricul-tural landscape in the Canadian prairies. The first approach was based on the implementation of nine types of digital terrain models (DTMs) and regression analysis of soil and topographic data. The second approach used a concept of accumulation, transit, and dissipation zones of the landsurface. Soil properties were soil moisture, residual phosphorus, solum thickness, depth to calcium carbonate, and organic carbon content. The dependence of soil properties on topography was supported by correlations for the upper soil layer. However, topographic control of soil moisture and residual phosphorus decreased with depth. Also, correlation coefficients and regression equations describing topographic control of soil moisture and residual phosphorus differed among sea-sons. This imposes limitations on regression-based predictions of the spatial distribution of soil properties. The prediction of soil property distribution with the concept of accumulation, transit and dissipation zones can be more successful and appropriate than the prediction based on linear regression. The variability in relationships between soil and topographic characteristics with depth may stem from spatial variability in the rate of decline of hydraulic conductivity with depth. Temporal variability in soil–topography relationships occurs because soil properties result from interactions of a variety of pedogenetic factors and processes marked by different temporal variability. In soil studies with digital terrain modelling, there is a need to take into account four types of
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".