Revealing the Controls of Soil Water Storage at Different Scales in a Hummocky Landscape
Bibliographic record
Abstract
Soil water storage is controlled by topography, soil texture, vegetation, water routing processes, and the depth to the water table. Interactions among these factors may give rise to scale‐dependent nonstationary and nonlinear patterns in soil water storage. The objectives of this study were to identify the dominant scales of variation of nonstationary and nonlinear soil water storage and delineate the dominant controls at those scales in a hummocky landscape using the Hilbert–Huang transform (HHT). Soil water storage (up to 140 cm) was measured along a 128‐point transect established at St. Denis National Wildlife Area, Saskatchewan, Canada, using time domain reflectometry and a neutron probe. Empirical mode decomposition was used to decompose the measured soil water storage series into six different intrinsic mode functions (IMFs) according on their characteristic scales. The first IMF represented the variations at small scales, the second IMF might characterize the variations associated with microtopography and the landform elements. The IMF 3 was highly correlated with elevation and had the largest variance contribution toward the total variance among all the IMFs. The fourth IMF was correlated to organic C (OC), showing the long‐term history of water availability, which may be a reflection of topographic setting or the elevation. The fifth and sixth IMFs were associated with elevation, soil texture, and OC but they contributed a small fraction of the total variance. Therefore, decomposition made through HHT was physically meaningful and provided improved prediction of soil water storage from topography, soil texture, and OC.
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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 | 0.000 |
| 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) | 0.000 | 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".