Wavelet coherency analysis to relate saturated hydraulic properties to soil physical properties
Bibliographic record
Abstract
A soil property may be related to another and the relationships may change depending on the scale and location. Understanding these scale‐ and location‐dependent relationships is important for prediction of one soil property based on another. The objective of this study is to use wavelet coherency analysis to examine whether the relationship between hydraulic properties and soil physical properties are scale‐ and location‐dependent. Undisturbed cores were collected along a transect from the sandy loam soil of a farm field in northern Saskatchewan, Canada. Saturated hydraulic conductivity (K s ), sand content, and organic carbon content (OC) were measured on these cores, and their relationships as a function of scale and location were analyzed using wavelets. Results indicated that the wavelet coherency between K s and sand content is only significantly different from that of red noises at the scales around 48 m. The cross‐wavelet spectrum and wavelet coherency are predominantly in phase, suggesting a positive correlation between K s and sand. For K s and OC, significant coherency exists at scales from 30 to 48 and around 80 m. At the scales of 30–48 and around 80 m the relationships are predominantly out of phase, suggesting negative correlation. Therefore relationships between K s and sand or K s and OC are not only scale‐dependent but also location‐dependent. Scale and location dependence have an important implication for understanding the scaling relationships between K s and sand and OC and for the prediction of K s from sand 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".