Comparison of Various Guelph Permeameter Analyses and Inverted Auger Hole Method at Different Depths in Estimation of Hydraulic Conductivity of Saturated Soil
Bibliographic record
Abstract
Hydraulic conductivity coefficient of saturated soil as one of its important physical properties indicates water movement in soil. However Guelph permeameter method is very simple, it has a robust theoretical fundamental. The main difficalty of the Guelph method is the double depth experiments which causes negative or irrational results in some of Kfs values because of its heterogeneous equations This difficalty can be resolved by single depth analyses of Guelph equation set. This research is based upon the results of single and multiple depth analysis of Guelph method in comparison to inverted auger hole method at three different depths. Hydraulic conductivity depth variation was assessed by both inverted auger hole and Guelph permeameter methods. The experiments were performed in 30 holes at three different depths of 60, 90 and 120 centimeters and simultaneously the samplings were done at the holes for exploration experiments. The experimental results show that Guelphpermeameter analyses estimates three times at 60 centimeter depth and five times at 90 and 120 centimeters depth less than the results got by inverted auger hole method. Laplace analysis gets higher values and the results made by basic regression analysis of Richards have the least variations and were close to the two depth analysis. The variation of hydraulic conductivity had a decreasing trend with depths This lands. But, this variation was not constant and its gradient decreases through depth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".