Role of in-situ point instruments in the estimation of variability in soil saturated hydraulic conductivity
Bibliographic record
Abstract
Abhishek Goyala* , Alessia Flamminib, Renato Morbidellib , Corrado Corradinib & Rao S. Govindarajuaa Lyles School of Civil Engineering, Purdue University, West Lafayette, Indiana, USAb Department of Civil and Environmental Engineering, University of Perugia, Perugia, ItalyCONTACT Abhishek Goyal abhishek@purdue.edu Lyles School of Civil Engineering, Lyles School of Civil Engineering, Purdue University, HAMP G108, West Lafayette, Indiana 47907, IN, USAABSTRACTSaturated hydraulic conductivity (Ks) is among the most important soil parameters for modeling hydrological processes, and its field-scale variability plays a significant role for soils subjected to light and moderate rainfalls. It is often assessed by either making point-scale measurements using permeameters and infiltrometers or coupling probabilistic models with field-scale infiltration experiments under natural/artificial rainfall conditions. The former requires numerous measurements across the study area, and the latter is constrained by the infiltration experiment’s rainfall pattern, thereby restricting these approaches to only partially resolving the spatial variability of Ks, over a field. This study investigated the applicability of three in situ infiltration devices – the double-ring infiltrometer, Commonwealth Scientific and Industrial Research Organisation (CSIRO) version of the tension permeameter, and Guelph permeameter – using a Bayesian framework. Results indicate the disparate estimates of Ks distribution parameters obtained from each instrument’s infiltration data. Using field-scale rainfall-runoff data, a posterior coarsening method is proposed to reconcile the point estimates from different instruments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 teacher head, 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".