Accounting for Bias and Boundary Condition Effects on Measurements of Saturated Core Hydraulic Conductivity
Bibliographic record
Abstract
Most hydrologic studies require knowledge of saturated soil hydraulic conductivity, K s This parameter is often measured using saturated soil cores and a constant applied hydraulic head device. A standard approach to reduce uncertainty in the result is to conduct replicate tests at a single hydraulic head gradient. Low‐permeability soils are often tested at large hydraulic head gradients to decrease measurement time. Our objective was to test the common assumptions implicit in calculating K s from constant‐head laboratory tests, i.e., the theoretical linear relationship between head gradient and flux density exists in experimental data, and the relationship passes through the origin. In this study, we used linear regression analysis to test these assumptions and determined K s from a broad range of head gradients for: 4.5‐cm (diameter) by 10‐cm (height) intact cores of sandy loam soil; 10‐cm (diameter) by 10‐cm (height) intact cores of clay loam soil; and repacked sand columns of various sizes. We found nonlinear relationships between hydraulic head gradient ( i ) and flux density ( q ) for tests conducted on intact cores of both soils, especially for head gradients greater than unity. When we calculated K s by linear regression of data from intact cores, we found average values approximately one‐third greater than the “standard” method of averaging several replicate tests at a single hydraulic head. The difference between the regression and standard analyses was attributed to experimental bias, which is removed by the linear regression. Although no consistent i or q “thresholds” were identified to predict the onset of nonlinearity in i vs. q data, the intact core results imply that i < 1 and q < 5 × 10 −3 cm s −1 may be advisable.
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 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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".