Spatial Variation of Soil Steady-State Infiltration Rates in Karst Hillslopes
Bibliographic record
Abstract
To find out the spatial variation of soil steady-state infiltration rates in karst hillslopes,the spatial distribution and soil properties of four habitats(soil surface,soil-rocky surface,rocky gully,and stone crevice)were investigated along six karst hillslopes with typical human activities.The soil steady-state infiltration rates were measured by means of a Guelph Permeameter.The statistical results show that:1)the soil steady-state infiltration rates of karst hillslopes are obviously high as compared to non-karst regions,and mainly controlled by soil porosities and cracks of Epikarst coupling;2)the soil steady-state infiltration rates of four habitats show high spatial heterogeneity andpatch shapespatial characteristics in karst hillslopes;and 3) transfer of human activities will modify soil properties of habitats,and then change the steady-state infiltration rates.The soil steady-state infiltration rates of soil surface and soil-rocky surface are higher than those of rocky gully and stone crevice in original forest,young forest and mixed arbor and shrub forest with smaller human influence.Soil bulk density and clay proportion of soil surface and soil-rocky surface increase seriously and contrary soil porosity and steady-state infiltration rates significantly decrease when the hillslopes are transformed to burned forest or pastureland.Under the same condition,soil properties and steady-state infiltration rates of rocky gullies and stone crevices affect less significantly than the soil surface and soil-rocky surface habitat,which still have high soil steady-state infiltration rates.
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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 | 0.001 |
| 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".