Application of the method of spatial point pattern analysis to the horizontal spatial distribution of preferential flow paths
Bibliographic record
Abstract
Preferential flow significantly influences hydrological processes in forests. The occurrence and development of this flow is directly affected by its spatial distribution. To determine whether or not point pattern analysis method can be used to examine the horizontal spatial distribution of preferential flow paths, experiments were conducted with dye tracer permeation to observe flow processes. Results indicated that an increase in penetration water volume exerted only a specific effect on preferential flow paths of large class in the topmost soil. Moreover, such paths showed distinct clumped patterns at the 25-cm scale under both high permeation water volume and low permeation water volume treatments. Nonetheless, the distribution pattern became uniform as scale range increased. The significance of the correlation between the spatial distribution of preferential flow paths and plant roots decreased from the top soil layer to the bottom. These findings suggest that soil depth and water permeation volume are important to the horizontal spatial distribution of preferential flow paths. Moreover, point pattern analysis method is suitable for investigating the horizontal spatial distribution of preferential flow paths and determining the correlation between the spatial distribution of preferential flow paths and plant roots.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".