A pedological investigation of soil erosion severity on undulating land in Lithuania
Bibliographic record
Abstract
It is essential that we can rapidly characterize soil erosion severity. This paper describes a field methodology to classify soil erosion severity on Dystric Albeluvisols in Lithuania. The goal was to assess cumulative soil loss due to the combined action of accelerated and natural soil erosion. Evaluation of soil erosion severity helps us understand which segments of the landscape are susceptible to erosion and therefore require soil conservation. Factors considered in evaluating soil erosion severity included the existing genetic soil horizons remaining after soil erosion processes, the estimated thickness of lost soil, and slope inclination. The estimated depth of soil loss due to the combined action of natural (geological) and accelerated soil erosion was 0.1–0.8 m on the undulating hilly topography of the Zemaiciai Uplands of Western Lithuania. Erosion rates increased with slope steepness. Soil erosion changed soil physical and chemical properties. Therefore, natural soil fertility, as indicated by spring barley yields, decreased 22, 40 and 62% on slopes of 2–5° (3.5–8.3%), 5-10° (8.3–17.7%) and 10–15° (17.7–26.3%), respectively, compared with flat land. Crop yield was strongly negatively correlated (R2 = 0.79, P < 0.001, n = 138) with erosion severity. Due to pedological translocation, non-eroded Dystric Albeluvisols had relatively little clay and silt in eluvial (E) soil horizons, with their relative accumulation in illuvial (Bt) horizons . Thus, severely eroded soils had argillaceous top soils, due to exhumation of Bt horizons. The suggested classification system enables rapid assessment of past soil erosion severity and may have broader applicability in areas of Podzolic soils. Key words: Dystric Albeluvisols, soil erosion severity, slope steepness, soil properties, pedology
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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 | 0.001 | 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".