Importance of terrain attributes in relation to the spatial distribution of soil properties at the micro scale: a case study
Bibliographic record
Abstract
Micro-topography and spatial variability of soil properties influence the environmental consequences of site-specific management. This study investigated the spatial structure of soil properties in relation to the micro-topography of an agricultural field in the Canadian Prairies. The geospatial sampling scheme had 178 soil cores to a depth of 120 cm. Soil texture and soil water content (SWC) at 0–120 cm, total nitrogen (TN), total carbon (TC), and soil organic carbon (SOC) at 0–15 cm were measured and spatially interpolated using semi-variograms calculated with GS+. The correlation of terrain attributes, calculated from digital elevation models, with soil properties was also assessed. Texture was strongly spatially dependent in the surface layers, and the significance of spatial dependency declined with depth. Spatial autocorrelation of sand content declined from 96% at the soil surface (0–15 cm) to 90% at 30–45 cm, 53% at 75–90 cm. SWC, TC, TN, and SOC were similarly auto-correlated. Elevation, relative slope position, and vertical distance to channel network influenced the distribution of texture and SWC based on analysis with partial least squares, though this relationship decreased with depth. Terrain attributes are correlated with the spatial variability of soil properties and should be considered in environmental analyses at the micro-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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".