Topographic influence on the variability of soil properties within an undulating Manitoba landscape
Bibliographic record
Abstract
As soil properties influence productivity, it is of interest to characterize their distribution for the purpose of intensified agricultural management in variable landscapes. Soil properties (soil organic C content, soil pH, A horizon thickness, solum thickness and depth to carbonates) were studied in 10 intensively sampled transects in a gently undulating glacial till landscape near Miniota, Manitoba. Using a landform description model, the study site was delineated into upper, mid and lower elevation landform element complexes (LEC). The program used a digital elevation model created from relative elevation data collected on a 10-m grid. Sample points were also stratified by soil series; Newdale (Orthic Black Chernozem), Varcoe (Gleyed Rego Black Chernozem) and Angusville (Gleyed Eluviated Black Chernozem) soils of the Newdale association were identified. Landform element complexes were ranked lower > mid > upper with respect to convergent landscape character. The eluviated Angusville profiles occurred under more convergent landscape character than the Newdale or Varcoe series. There was a consistent rank of lower > mid > upper with respect to depth to carbonates, A horizon thickness, solum thickness and soil organic C content. Relative ranking of the pH in the Ap horizon was the opposite. In all cases, the lower LEC emerged as most clearly distinct. There was substantial variability in soil profile development, and, therefore, soil series membership, within individual LEC. This indicated that the scale at which LEC are delineated is broader than that at which soil series variability occurs. Nonetheless, LEC were useful in capturing gross variability in soil properties within the landscape at a scale that would allow unique agricultural management practices. Key words: Soil-landscape, solum thickness, depth to carbonates, organic carbon, soil pH
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".