Temporally stable patterns but seasonal dependent controls of soil water content: Evidence from wavelet analyses
Bibliographic record
Abstract
Abstract Scale‐ and location‐dependent relationships between soil water content (SWC) and individual environmental factors have been widely explored. SWC is controlled by multiple factors concurrently; however, the multivariate relationship is rarely explored at different scales and locations. Multivariate controls of SWC at different scales and locations in two seasons within a hummocky landscape of North America were identified using bivariate wavelet coherency and multiple wavelet coherence. Results showed that depth to CaCO3 layer, which was correlated with elevation over all locations at scales of 36–144 m and cos(aspect), provided the best individual factor for explaining SWC variations in spring (May 2) and summer (August 23), respectively. Although spatial patterns of SWC were temporally stable, different topographic indices affected spatial distribution of SWC in different seasons (elevation in spring and aspect in summer) due to different dominating hydrological processes. These varying hydrological processes also resulted in the distinct role of soil organic carbon (SOC) content in different seasons: a positive correlation in spring and a negative correlation in summer. Multiple wavelet coherence identified a combination of depth to CaCO3 layer and SOC in spring and a combination of cos(aspect) and SOC in summer that controlled SWC at different scales and locations, respectively. This indicated a combined effect of soil and topographic properties on SWC distribution and a clear need for these two factors in developing scale‐dependent prediction of SWC in the hummocky landscape of North America.
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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 | 0.002 |
| 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) | 0.000 | 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".