Bibliographic record
Abstract
Different field and laboratory methods for determining the hydraulic properties of unsaturated soils were studied. Drying moisture retention curves were measured on undisturbed soil samples with a pressure plate apparatus, and wetting soil moisture retention curves were measured on repacked soil columns via a capillary rise experiment Saturated hydraulic conductivities were measured in-situ with a Guelph permeameter, and in the laboratory with falling head tests. Finally, an inverse modeling technique was used to analyze transient flow data from in-situ cone permeameter and laboratory multi-step outflow experiments to simultaneously obtain wetting and drying curves for both the moisture retention and the hydraulic conductivity functions. The retention curves obtained from analysis of the cone permeameter data were consistent with results of the multi-step outflow tests and bounded by the capillary and pressure plate test results, as expected. The retention curves derived from cone permeameter data exhibited more curvature than those obtained from the other methods. The curves obtained from multi-step outflow tests exhibited less hysteresis than those derived from cone permeameter tests and were more repeatable than the pressure plate test results.
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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".