Bibliographic record
Abstract
Understanding the spatial and statistical distribution of soil water flux in a field is fundamental for stochastic modeling soil water flow and chemical transport in spatially variable soils. The objective of this study was to examine the persistence of the spatial pattern and statistical distribution of local soil water flux for different application rates during constant flux rainfall infiltrations. A series of constant flux‐infiltration experiments were conducted in a spatially variable field. The local soil water fluxes for each of the 0‐ to 0.2‐, 0‐ to 0.4‐, 0‐ to 0.6‐, and 0‐ to 0.8‐m depths were determined from the change of water storage as a function of time before the wetting front passes the end of vertically installed time domain reflectometry (TDR) probes. The spatial similarity (persistent spatial pattern) of the measured soil water flux for different application rates at four depths was examined using Spearman rank correlation coefficient. Results showed that there was no persistent spatial similarity among measured soil water fluxes for the 0‐ to 0.2‐, 0‐ to 0.4‐, 0‐ to 0.6‐, and 0‐ to 0.8‐m depths. This indicates that transient infiltration experiments with different application rates have different flow pathways for each of the 0‐ to 0.2‐, 0‐ to 0.4‐, 0‐ to 0.6‐, and 0‐ to 0.8‐m depths. The statistical similarity (persistent statistical distribution) of soil water flux for different application rates was examined using histograms. Chi‐square tests indicated that the histograms of soil water flux for different application rates were different for each of the 0‐ to 0.2‐, 0‐ to 0.4‐, 0‐ to 0.6‐, and 0‐ to 0.8‐m depths, suggesting the stochastic convective flow model may not be used to predict flow and transport in this field for different application rates.
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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) | 0.001 | 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".