Mapping topsoil field-saturated hydraulic conductivity from point measurements using different methods
Bibliographic record
Abstract
Abstract Topsoil field-saturated hydraulic conductivity, Kfs , is a parameter that controls the partition of rainfall between infiltration and runoff and is a key parameter in most distributed hydrological models. There is a mismatch between the scale of local in situ Kfs measurements and the scale at which the parameter is required in models for regional mapping. Therefore methods for extrapolating local Kfs values to larger mapping units are required. The paper explores the feasibility of mapping Kfs in the Cévennes-Vivarais region, in south-east France, using more easily available GIS data concerning geology and land cover. Our analysis makes uses of a data set from infiltration measurements performed in the area and its vicinity for more than ten years. The data set is composed of Kfs derived from infiltration measurements performed using various methods: Guelph permeameters, double ring and single ring infiltrotrometers and tension infiltrometers. The different methods resulted in a large variation in Kfs up to several orders of magnitude. A method is proposed to pool the data from the different infiltration methods to create an equivalent set of Kfs . Statistical tests showed significant differences in Kfs distributions in function of different geological formations and land cover. Thus the mapping of Kfs at regional scale was based on geological formations and land cover. This map was compared to a map based on the Rawls and Brakensiek (RB) pedotransfer function (mainly based on texture) and the two maps showed very different patterns. The RB values did not fit observed equivalent Kfs at the local scale, highlighting that soil texture alone is not a good predictor of Kfs .
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".