Determining and Comparing Deep Drainage between Two Years – Dry (2012) and Wet (2013) – using Darcy’s Law
Bibliographic record
Abstract
Understanding of the downward flux of water below the plant root zone, known as deep drainage (DD), is significant in agriculture and soil water conservation. It plays a key role to determine the amount of water that travels below the plant root zone and can potentially cause groundwater recharge. The DD in soil varies with location, soil texture, and topography. Thus, the objectives of this study were to determine the unsaturated hydraulic conductivity, soil water storage, and DD for the years 2012 (dry year) and 2013 (wet year) at the University of Guelph’s Arboretum. The depths to the water table data were collected using a Mini Water Level Meter. CS616 sensors were used to determine the soil volumetric water content. The soil temperature was extracted with the use of T107 Temperature Probes. The slug test, based on the Hvorslev method, was performed to determine the field saturated hydraulic conductivity. The soil moisture retention curve was produced based on the data collected in the lab with the use of pressure plate systems, using van Genuchten’s equation. The unsaturated hydraulic conductivity was also determined using van Genuchten’s equation. Darcy’s law was used to determine the specific discharge, which was then converted to the total DD. In general, the soil water storage was 38.5 mm higher in 2013 relative to 2012. The unsaturated hydraulic conductivity was approximately 2 times higher in 2013 than 2012. The average DD was approximately 25 mm higher in 2013. This study provides information needed to better understand the movement and amount of water flux and DD in larger details.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".