Infiltration and drainage processes in multi-layered coarse soils
Bibliographic record
Abstract
Huang, M., Barbour, S. L., Elshorbagy, A., Zettl, J. D. and Si, B. C. 2011. Infiltration and drainage processes in multi-layered coarse soils. Can. J. Soil Sci. 91: 169–183. Infiltration and drainage processes in multi-layered soils are complicated by contrasting hydraulic properties. The objective of this study was to evaluate the performances of the hysteretic and non-hysteretic models to simulate the infiltration and drainage processes from three different natural soil profiles containing as many as 20 texturally different layers. Hydraulic properties were estimated from soil textures using pedotransfer functions and were calibrated and validated using measured water contents during infiltration and drainage phases, respectively. The results supported the use of the Arya-Paris pedotransfer function to estimate the wetting curve when contact angles are incorporated. The unique Kozeny-Carmen equation parameter was evaluated by optimizing the estimated saturated hydraulic conductivity. The calibrated numerical model (Hydrus-1D) accurately simulated soil water content profiles and water volumes during the infiltration and drainage phases. The mean error of prediction (MEP) between the measured and estimated soil water contents varied from –0.030 to 0.010 cm3 cm−3, and the standard deviation of prediction (SDP) from 0.003 to 0.057 cm3 cm−3. The simulation was improved for more heterogeneous soil profiles when hysteresis was taken into account. The measured and simulated results indicated that the soil profile with vertical heterogeneity in soil texture can store more water than the similar textured vertically homogeneous soils under drained conditions.
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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 | 0.000 |
| 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 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".