Multiscale Characterization of Vadose Zone Macroporosity in Relation to Hydraulic Conductivity and Subsurface Drainage
Bibliographic record
Abstract
The purpose of this study was to quantify macroporosity distribution in relation to tile drains, and to determine any correlation between macroporosity and hydraulic conductivity (K). Macropore−tile drain relations were investigated at three plots within a single field. Macropores on 149 0.25‐m 2 horizontal surfaces, located at depths of 0.02 to 1 m, and distances of 0 to 3.5 m from the tile drains, were counted and classified into 0.5 to 5, 5 to 8, 8 to 10, and 10 to 12 mm equivalent circular diameter categories. Results show no significant ( P < 0.1) difference in macropore area fraction (MAF) (defined as macropore area/total soil area) distribution relative to tile drain position; however, MAF did vary significantly between plots. The coefficient of variation for plot‐specific, depth‐averaged, total MAF ranged from 10 to 48%. Macropores in the 0.5‐ to 5‐mm and 5‐ to 12‐mm size ranges were most abundant near the surface and the shallow B horizon, respectively. When present, macropores at tile depth were mostly 0.5 to 5mm in size. Maximum depth‐averaged total MAF ranged from 0.0036 to 0.0087 within the plots, whereas the minimum depth averaged MAF was less than 0.001 and located at tile depth. Strong correlation was observed between field surface K and MAF below 0.45 m depth ( r = 0.79), and B horizon K and MAF below 0.02 m depth ( r ≥ 0.83). Dye staining patterns revealed that >20‐yr‐old tile installation scars were channeling infiltrate to tile drains at two plots.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".