Predicting hydraulic conductivity changes from aggregate mean weight diameter
Bibliographic record
Abstract
Rapid wetting of structurally unstable soils results in aggregate disintegration, soil densification, reduced porosity, and changes in the pore‐size distribution and intake hydraulic properties. Knowledge of changes in these properties is critical for use in hydrological, solute transport, or erosion models. Multiple determinations of these parameters as they evolve over time are needed for short time step models. Routine measurement of all these properties is time‐consuming, and it would therefore be practical if some of them could be used to predict others. A model is proposed to predict hydraulic conductivity changes based on aggregate size distribution, stability, and wetting rate information in order to determine the stability of the pore network during wetting. Two independent experiments were conducted in the laboratory on two different soil types to evaluate the proposed model. The decrease in mean weight diameter of aggregates exposed to different rates of wetting was used to predict the mean weight diameter after wetting, which was then used in a model to predict changes in hydraulic conductivity. The results show that the changes in predicted and measured hydraulic conductivities between the potentials of −0.5 and −4 kPa were significantly correlated for both soils, despite an overestimation of the measured changes. The close correlation suggests that this bias could be empirically estimated from measurements of aggregate size and that information on aggregate size and stability would be useful for predicting changes in hydraulic conductivity.
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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 | 0.001 | 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".