Analysis of spatial variability of soil water retention using the cumulative distribution function matching
Bibliographic record
Abstract
The importance of modeling dependencies of spatial variability of soil water content on soil matric potential grows due to the proliferation of ensemble modeling, data assimilations, and other soil water modeling applications. The objective of this work was to investigate conversions between cumulative distribution functions of water contents at different matric potentials. In total, 80 samples were taken in the nodes of the grid to measure soil water retention using sand and sand–kaolin capillarimeters at absolute values of soil matric potential of 0.001, 0.003, 0.010, 0.020, and 0.050 MPa, and with the water vapor desorption method at 3, 21, 39, 82, and 142 MPa. The probabilities of distributions of both non-transformed and log-transformed soil water contents being normal appeared to be larger than 0.05 in most cases. Using the probit function to represent the observed variability allowed us to match cumulative probability distributions at different soil water potentials. Slopes of dependencies of probits on non-transformed and log-transformed water contents had one-parametric linear dependencies on the logarithms of the absolute value of soil matric potential in capillary and adsorptive potential ranges.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".