Spatial and temporal variability of soil horizons and long-term solute transport under semi-arid conditions
Bibliographic record
Abstract
Woods, S. A., Dyck, M. F. and Kachanoski, R. G. 2013. Spatial and temporal variability of soil horizons and long-term solute transport under semi-arid conditions. Can. J. Soil Sci. 93: 173-191. Characterizing the spatial and temporal variability of deep drainage is required for quantifying risks to groundwater resources associated with chemicals released into the soil. A variety of approaches are available to characterize the spatial variability of deep drainage, including complex, spatially explicit hydrological models or simpler, distributed soil water balance models. There is no clear understanding which approach is most appropriate for a given landscape. In this paper we compare the spatial distribution of an applied chloride tracer to pedogenic nitrate and sulphate salts, subject to transport in the soil over decadal to millennial time scales, to characterize the relative spatial and temporal differences in deep drainage at a site in southern Saskatchewan. Comparison of the spatial distribution of the salts with differing soil residence times showed that the soil water balance and deep drainage fluxes have changed significantly over time in some parts of the landscape because of infilling of surface depressions as indicated by the presence of buried A horizons. At larger scales, the distribution of the salts showed very little correspondence to the spatial distribution and thicknesses of soil horizons (often used to infer spatial variability in soil water balance), but was more consistent with the scale of the surface topography. Thus it was concluded that spatial and temporal changes in surface topography (i.e., catchment area) were the primary factors responsible for the observed transport of the salts. We propose that this site is representative of the cold, semi-arid prairies and that these conclusions likely apply to this region.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".