Preferential Flow Revealed by Hydrologic Modeling Based on Predicted Hydraulic Properties
Bibliographic record
Abstract
Pedotransfer functions have shown a reasonable reliability and accuracy for predicting soil hydraulic properties. However, the contribution of the range of macroscopic features leading to preferential water flow is not readily taken into account. We modified and used the hydrological component of the reactive transport model MIN3P and the neural network‐based code ROSETTA in an attempt to simulate 4 yr of daily measurements of the soil water content in a forest soil covered by Douglas‐fir ( Pseudotsuga menziessii Franco). A good fit of the mean measured water contents was obtained during periods of low soil moisture, while the model tended to overpredict water contents during periods of high soil moisture. This behavior is typical for the presence of significant preferential flow. Slightly better results were obtained by using predicted values of the saturated hydraulic conductivity, while the assumption of a water table located at shallow depth increased discrepancies. A good match was obtained by calibration of a simple preferential flow scheme, which was based on the assumption that the retention properties of the porous network control preferential flow. Accordingly, preferential flow seemed to initiate within the capillary pore domain. This causes a much greater sensitivity of the results to the position of the water table than with other schemes that consider pure gravity‐driven flow in large macropores. Knowledge of the functional pore size is needed to ascertain the type of preferential flow scheme to be used.
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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 | 0.001 |
| 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 | 0.001 |
| 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 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".