Scaling Relationships between Saturated Hydraulic Conductivity and Soil Physical Properties
Bibliographic record
Abstract
Saturated hydraulic conductivity ( K s ) is an important soil hydraulic property that affects water flow and the transport of dissolved solutes. Obtaining sufficient and reliable K s data for large‐scale process modeling is always a challenge due to the extremely high spatial variability. The objectives of this study were (i) to determine if a monofractal or multifractal approach is needed to describe the variability in K s and its soil surrogates, and (ii) to identify which soil property best reflects the spatial distribution of K s across a wider range of scales. Saturated hydraulic conductivity and soil physical property data were collected from a 384‐m transect, located at Smeaton, SK, Canada. Observation scale variability and relationships were examined using statistical and geostatistical methods. Statistical scale‐invariance was evaluated through the Hurst scaling parameter ( H ). Multiple scale variability and relationships were studied using multifractal and joint multifractal techniques. Results indicate that for all the studied variables 0.80 < H < 0.90, suggesting a certain degree of statistical scale‐invariance and long‐range dependency. At the observation scale, the variability in K s was significantly related to sand (SA) and silt (SI) distribution ( R = 0.40 for SA and −0.39 for SI, P < 0.01; n = 128), whereas, across a wider range of scales, the variability in K s was related only to clay (CL) and organic C (OC). The result indicates scale dependent relationships between K s and soil physical properties, which implies that the success of predictive models such as pedotransfer functions (PTFs) and K s aggregation techniques depends largely on the correspondence between observation and implementation scales.
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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 | 0.004 |
| 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 | 0.001 |
| 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".