Spatial relationship between soil hydraulic and soil physical properties in a farm field
Bibliographic record
Abstract
The relationship between soil properties may vary with their spatial separation. Understanding this relationship is important in predicting hydraulic parameters from other soil physical properties. The objective of this study was to identify spatially dependent relationships between hydraulic parameters and soil physical properties. Regularly spaced (3-m) undisturbed soil samples were collected along a 384 m transect from a farm field at Smeaton, Saskatchewan. Saturated hydraulic conductivity, the soil water retention curve, and soil physical properties were measured. The scaling parameter, van Genuchten scaling parameter α (VGα), and curve shape parameter, van Genuchten curve shape parameter n (VGn), were obtained by fitting the van Genuchten model to measured soil moisture retention data. Results showed that the semivariograms of soil properties exhibited two different spatial structures at spatial separations of 20 and 120 m, respectively. A strong spatial structure was observed in organic carbon, saturated hydraulic conductivity (Ks), sand, and silt; whereas a weak structure was found for VGα and VGn. Correlation circle analysis showed strong spatially dependent relationships of Ks and VGα; with soil physical properties, but weak relationships of θs and VGn with soil physical properties. The spatially dependent relationships between soil physical and soil hydraulic parameters should be taken into consideration when developing pedotransfer functions. Key words: Spatial relationship, geostatistics, linear coregionalization model, principal component analysis, pedotransfer function
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".