Impacts of changes in vegetation on saturated hydraulic conductivity of soil in subtropical forests
Bibliographic record
Abstract
Abstract Saturated hydraulic conductivity ( K s ) is one of the most important soil properties that determines water flow behavior in terrestrial ecosystems. However, the K s of forest soils is difficult to predict due to multiple interactions, such as anthropological and geomorphic processes. In this study, we examined the impacts of vegetation type on K s and associated mechanisms. We found that K s differed with vegetation type and soil depth, and the impact of vegetation type on K s was dependent on soil depth. K s did not differ among vegetation types at soil depths of 0–10 and 20–30 cm, but was significantly lower in managed forest types (mixed evergreen broad-leaved and coniferous forests, bamboo forests, and tea gardens) than native evergreen broadleaf forests at a depth of 10–20 cm. Boosted regression tree analysis indicated that total porosity, non-capillary porosity, and macro water-stable aggregates were the primary factors that influenced K s . Our results suggested that vegetation type was a key factor that influences hydraulic properties in subtropical forest soils through the alteration of soil properties, such as porosity and macro water-stable aggregates.
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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".