Water Flow in Unsaturated Soil Below Turfgrass Observations and LEACHM (within EXPRES) Predictions
Bibliographic record
Abstract
In cropped soils, water sustains the plants, affects the transport of nutrients within the root zone, and controls the leaching of nutrients and chemicals to ground water. The objectives of this study were (i) to investigate the effects of turfgrass on water flow in sandy loam soil during the growing season using field lysimeters, and (ii) to test the abilities of the models EXPRES and LEACHN with free‐drainage and lysimeter bottom‐boundary conditions, respectively, to simulate water movement in the lysimeters. Twelve field lysimeters were packed with a three‐horizon profile, topped with Kentucky bluegrass ( Poa pratensis L.) sod, and monitored for 2 yr. Saturated hydraulic conductivity, measured on cores, was much greater and more variable for turf than soil. The moisture‐retention curve for turf also had a much steeper drop in water content at low applied negative head than soil. The lysimeters became very dry during the summer, and only drained during the spring and autumn. The model EXPRES generally predicted water flow well, but had some difficulty with water redistribution during the drying periods (gravity drainage and evapotranspiration). In general, with the free‐drainage bottom‐boundary condition, EXPRES predicted more drainage and less drying during the summer than was observed. Under conditions of little to no irrigation, the free‐drainage condition over‐predicted and the lysimeter condition under‐predicted the total amount of measured drainage. Model predictions of drainage under heavily irrigated conditions were similar for both bottom‐boundary 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 | 0.000 |
| 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".