Water Flow in Unsaturated Soil Below Turfgrass Observations and LEACHM (within EXPRES) Predictions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it