Evaluation of Infiltrometers and Permeameters for Measuring Hydraulic Conductivity
Bibliographic record
Abstract
Abstract Soil hydraulic conductivity is a mandatory input for determining water and solute transport through soils. There are several well-established infiltrometers and permeameters for measuring in situ hydraulic conductivity. Infiltrometers measure hydraulic conductivity based on water entry into an unsaturated soil at the soil-atmosphere boundary, whereas permeameters measure the flow of water from one point to another within the soil mass. This difference in measurement philosophy, along with the methods of analysis involved in the measurement, may result in varying estimates of in situ hydraulic conductivity. This study performs an evaluation among three infiltrometers (double ring infiltrometer [DRI] and two disc infiltrometers) and two permeameters (Guelph permeameter [GP] and laboratory permeameter) for measuring hydraulic conductivity. The primary objective of this study is to appraise the variability in the measurement of in situ hydraulic conductivity for identical field conditions using different infiltrometers and permeameters. The study indicated that all the permeameters and infiltrometers exhibited reasonably good repeatability in measurements. Unlike infiltrometers, the hydraulic conductivity determined from permeameters was found to exhibit similar values for two different seasons. Infiltrometers were found to be highly sensitive to alteration in the surface pore structure due to the soil-atmosphere interaction. The statistical evaluation indicated a negative bias of disc infiltrometers when compared with DRI, whereas the comparison of disc infiltrometers has shown a bias close to zero. The results of the GP closely compared with laboratory permeameter. Both the disc infiltrometers exhibited a negative bias and weak correlation with GP measurements. In the absence of parity between infiltrometer and permeameter, the former may be a better choice for including the effect of soil surface alteration on hydrological modeling, whereas the latter can be handy for modeling water redistribution within the soil mass.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".