Evaluation of Different Methods for Measuring Field Saturated Hydraulic Conductivity under High and Low Water Table
Bibliographic record
Abstract
In this research, four methods of in situ measurement of saturated hydraulic conductivity ( K s ) including the Guelph permeameter (GP), auger hole (AH), original Porchet (OP), and saturated Porchet methods were compared for a silt loam soil. The representative K s in a drainage system was also determined as a reference value. The mean values of K s for the GP, AH, OP, SP, and drainage system methods were 1.18, 1.06, 1.85, 1.18, and 1.08 m d −1 , respectively. Furthermore, the GP and OP methods had the highest and lowest coefficients of variation at 42.1 and 24.2%, respectively. The difference in K s values among all methods except SP and GP and the difference in CV values between GP and OP were statistically significant at the 0.01 level of probability. The direct measurement from the drainage system resulted in K s values of 0.584, 0.915, 1.019, and 0.915 times that of the OP, SP, AH, and GP methods, respectively. The results of the SP, GP, and AH methods were very similar to that obtained from direct measurement of the drainage system. Results showed that the AH and GP methods are the best methods for measuring K s in the presence and absence of a water table, respectively, with −1.85 and 9.26% difference, respectively, compared with the reference method. Therefore, in regions with a high water table, the AH method is suitable, and for a low water table, the GP method is recommended.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".