Evaluation of Macropore Flow and Transport Using Three‐Dimensional Simulation of Tension Infiltration Experiments
Bibliographic record
Abstract
Macropores are important hydrologic features that result in preferential flow and transport even under partially saturated flow conditions. The objective of this study was to use numerical simulations to investigate the hydraulic representation of preferential flow in partially saturated macroporous soils. Tension infiltration experiments that exhibited varying degrees of preferential flow, primarily along worm burrows, provided the basis for the numerical simulations. Field measurements of infiltration, soil water content, and dye transport were used to calibrate the model and assess the results. A three‐dimensional model was constructed such that the soil matrix contained discrete vertical macropores. The simulations were able to capture the relevant flow and transport characteristics during infiltration. Sensitivity analyses demonstrated the utility of cumulative infiltration and dye transport data for constraining numerical simulations of macroporous systems. Hydraulic conductivity estimates for both matrix and macropores were lower than expected, which may be due to an overly simplified description of macropore flow hydraulics. Simulated macropore discontinuities near the surface reduced the infiltration volume by >50% and the depth of dye transport by >80%. The simulations also showed that increasing macropore density was nearly linearly related to increases in preferential flow, and confirmed field observations that closer macropore spacing led to increased transport depths due to macropore–matrix interaction and conjoined wetting fronts between neighboring macropores. This discrete macropore approach provides a useful method for examining macropore flow and transport, and highlights gaps in our understanding of the unsaturated flow behavior of macropores.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".