On the estimation of saturated hydraulic conductivity: from local to field scale
Bibliographic record
Abstract
This thesis presents a comparison of Ks estimates obtained by three classical devices, namely the double ring infiltrometer (DRI), the Guelph version of the constant-head well permeameter (GP) and the CSIRO version of the ten- sion permeameter (CTP). A distinguishing feature of this study is the use of steady deep flow, obtained from controlled rainfall-runoff experiments, as a benchmark of Ks at "local" and field/plot scales to assess the reliability of the above methods. \nSpatially representative estimates of Ks are needed for simulating catchment scale surface runoff and infiltration. Classical methods for measuring Ks at the catchment scale are time-consuming. Important insights can be obtained by experiments aimed at understanding the controls of Ks in an agricultural setting and identifying the minimum number of samples required for estimating representative plot scale Ks values. This thesis presents results from a total of 131 double-ring infiltrometer measurements at 12 plots in a small Austrian catchment. \nClassical field techniques to determine Ks at the plot and catchment scales are complex and time-consuming, therefore the development of pedotransfer functions, PTFs, to derive Ks from easily available soil properties is of utmost importance. However, PTFs have been generally developed at the point scale, while application of hydrological modeling requires field scale estimates. In this thesis, values of field-scale saturated hydraulic conductivity, K ̄s , measured in a number of areas within the Austrian catchment, have been used to derive two PTFs by multiple linear regression (PTFMLR) and ridge regression (PTFR). Two alternative approaches have been used: (A) soil properties have been first interpolated with successive application of the PTFs, (B) the PTFs have been first applied in the sites where soil properties were available and then interpolated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".