Variabilidad espacial de la infiltración de una ladera determinada con permeámetro de Guelph e infiltrometro de tensión
Bibliographic record
Abstract
The aim of this work is to study soil water infiltration and its variability at the hillslope level. Saturated hydraulic conductivity data obtained by two different methods, i. e. Guelph permeameter and tension infiltrometer, were compared. In addition, also unsaturated hydraulic conductivity data obtained by tensioinfiltrometry were analyzed. Field data were taken according to a regular grid pattern on a fallow soil, after harvesting of a winter oats-vetch crop. Both methods generate log-normally distributed data that exhibit a wide range of values, but comparison with literature values show that this is normal. The unsaturated hydraulic conductivity data were used to show that both, macroporosity and soil moisture content, were possible parameters that influenced the amount of (spatial) variation in hydraulic conductivity. It appeared that the methods are not comparable on a one-by-one basis but still on the sample level. Geostatistical analysis showed that there exists a spatial structure of the infiltration data as measured with both methods. When the saturated hydraulic conductivity is interpolated with three methods, i.e., inverse distance, kriging and conditional simulation, it appeared that the inverse distance weighted interpolation is the easiest method to use. The more sophisticated method of Gaussian conditional simulation can best be used, guided by ordinary point kriging to obtain information about uncertainties in the interpolation as well. Advantages and disadvantages of the three interpolation methods are discussed.
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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 | 0.001 |
| 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 | 0.000 |
| 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 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".