How Spatial Patterns of Soil Moisture Dynamics Can Explain Field‐Scale Soil Moisture Variability: Observations From a Sodic Landscape
Bibliographic record
Abstract
Abstract Root‐zone soil moisture (0–110 cm) was monitored at 21 sites within a cold‐region semiarid prairie grazing pasture over multiple growing seasons. There were large differences in the moisture dynamics for different sites, which was related to local‐scale impacts of sodium‐induced clay dispersion. Sites with high exchangeable sodium percentages, indicative of sodic soils, were characterized by small changes in soil moisture storage. The sites with the largest soil moisture changes had negligible exchangeable sodium percentage. We used this difference to divide the area into sites that participate in the field‐scale water balance and those that do not. As a result of this heterogeneity, a unique soil moisture variability‐mean relationship was observed; the spatial variability of root‐zone soil moisture was lowest during intermediate wetness conditions and highest for wet and dry conditions. Furthermore, the shape of the variability‐mean relationship depended on the depth over which soil moisture was integrated. The persistent differences in soil moisture dynamics were also responsible for the presence of two distinct spatial patterns of root‐zone soil moisture, representing early and late growing season (i.e., wet and dry conditions). The participating versus nonparticipating framework may be applicable broadly to sodic soils and to other soil types and has practical implications for parameterizing the water balance in hydrological models and for designing representative soil moisture monitoring networks in such environments.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".