Peculiarities of Infiltration Measurements in Water-Repellent Forest Soil
Bibliographic record
Abstract
The paper deals with measurements of water infiltration carried out on a well-developed forest floor formed by needle-leaf litter of Norway spruce. Three field methods (tension disk permeameter, single-ring infiltrometer and Guelph permeameter) were used to determine the soil hydraulic conductivity. The results were strongly influenced by the water repellency at the interface between the O- and A-horizons. This interface was severely water repellent during the hot and dry summer season, regardless of the generally humid mountain climate of the High Tatras foothill. The single-ring method paradoxically provided lower hydraulic conductivity (3.2 × 10−4 ± 1.3 × 10−4) compared to the tension disk permeameter (8.5 × 10−4 ± 3.3 × 10−4) due to the presence of the water-repellent O/A-interface. This effect was also observed with the Guelph permeameter method, which gave the lowest value (5.6 × 10−5 ± 4.3 × 10−5). Abrupt retardation of infiltration on the water-repellent interface may generate shallow subsurface runoff (as was proved by the irrigation experiment) or litter splash during extreme rainfall events and promote water flow to deeper soil horizons through preferential pathways. The observed effects of the forest floor on rainfall infiltration will depend on the seasonal variability of soil water repellency. Although the forest floor is a source of hydrophobic substances that cause water repellency at the O/A-interface and can trigger runoff generation, at the same time its cohesive duff layer protects the forest soil from erosion.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".