Depth Persistence of the Spatial Pattern of Soil Water Storage in a Hummocky Landscape
Bibliographic record
Abstract
Information on surface soil water is readily available either from satellite images or from other surface measurements. Understanding the relationships between soil water at the surface and subsurface layers can help understand hydrological processes at depth. The objective of this study was to examine the similarities in the overall and scale specific spatial patterns of soil water storage at different depths. Soil water content was measured at the 20-cm depth increments, from the surface to a depth of 140 cm, using a neutron probe and time-domain reflectrometry along a transect traversed over several knolls and depressions at St. Denis National Wildlife Area (SDNWA), Saskatchewan, Canada. High soil water storage was observed in depressions and low water storage on knolls creating an inverse spatial pattern relative to elevation. High Spearman rank correlation coefficients between the surface and subsurface soil layers indicated strong similarity in the overall spatial pattern of soil water at different depths. Soil water contents in layers close in vertical distance had stronger similarity than that of layers far apart. Wavelet coherency analysis indicated strong similarity in the large-scale (>72 m) spatial patterns of soil water at the surface layer and deeper layers during recharge period. However, large-scale similarity was weaker during discharge period than during recharge period. The small- and medium-scale similarity changed with depths during both recharge and discharge periods. The scale specific similarity in the spatial pattern of soil water can be used to guide estimating subsurface soil water at different depths from the surface soil water.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".