Determining Soil Hydraulic Properties from Tension Infiltrometer Measurements
Bibliographic record
Abstract
Tension infiltrometer measurements have been used to measure steady‐state infiltration rates at applied tensions. The measurements can be used to determined soil hydraulic properties through linear or nonlinear regression. Such regression methods are often based on a few imprecise field measurements, and the traditional regression analysis may not yield valid estimates and reliable predictions. The objective of this study is to introduce fuzzy linear regression as an alternative to statistical regression analysis in determining hydraulic properties from tension infiltrometer measurements. Using a tension infiltrometer, in situ steady‐state infiltration rates [ q ∞ ( h )] were measured at six different tensions ( h ) between 3 and 22 cm of water on silty loam and clay loam soils. Hydraulic properties (i.e., field saturated hydraulic conductivity, K fs , and inverse macroscopic capillary length scale, α G ) and their confidence intervals were estimated following the fuzzy least‐square linear regression procedure with the minimum fuzziness criterion and linear least‐squares method (traditional statistical method). Both calculation procedures yielded the same mean hydraulic properties. A comparison between fuzzy and statistical ln q ∞ ( h ) relationship indicated that the confidence bands resulting from both procedures enveloped all the measurement points, but fuzzy regression estimates offered a tighter fit around the midpoint values (least‐square estimates). Fuzzy linear regression is more reliable and may be used as a complement or an alternative to statistical linear regression analysis for determining hydraulic properties from tension infiltrometer measurements.
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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 | 0.001 |
| 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".