Problems of Hydraulic Conductivity Estimation in Clayey Karst Soils
Bibliographic record
Abstract
Even in karst areas, considerably thick soils can be found in accumulationzones. Here, the degree of groundwater vulnerability dependsnot only on the thickness, but also on the hydraulic conductivity andretention properties of the soil cover. The hydraulic conductivity offine-grained karst soils from Slovakia, Croatia and Austria was studiedwithin several international research projects, by the applicationof four different test methods. Results are discussed from differentpoints of view. Triaxial tests yielded a very broad interval between themaximum and minimum hydraulic conductivity (from 5.83x10-7 m.s-1to 3.50x10-11 m.s-1), therefore the mean value cannot be used in anycalculations. The consolidometer method gave lower values in general,between 9.40x10-10 m.s-1 to 3.59x10-8 m.s-1. However, this methodoverestimates the soil “impermeability”. Estimates based on grainsize are unsuitable, as fine-grained soils did not fulfil the random conditions of known formula. Finally, the “in situ” hydraulic conductivitywas measured using a Guelph permeameter. As expected, “in situ”tests showed 100 to 1000-times higher kf than the laboratory tests.This method best reflects the real conditions. Therefore, only thistype of data should be considered in any environmental modelling.In a soil profile, hydraulic conductivity depends on the mineral composition, depth, secondary compaction, etc. The degree and durationof saturation with water is very important for young soils containingsmectite. Their hydraulic conductivity might be very low when saturatedfor long time, but also very high, when open desiccation cracksoccur. A very slight trend was found, but only in Slovak soils, showinga decrease in the hydraulic conductivity with increasing contentof the clay fraction <0.002 mm. These results should contribute to abetter estimate of the protective role of soils in groundwater vulnerabilitymaps.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".