Dye staining and excavation of a lateral preferential flow network
Bibliographic record
Abstract
Abstract. Preferential flow features have been found to be important for runoff generation, solute transport, and slope stability in many areas around the world. Although many studies have identified the particular characteristics of individual features and measured the runoff generation and solute transport within hillslopes, no studies have determined how individual features are hydraulically connected at a hillslope scale. In this study, we used dye staining and excavation to determine the morphology and spatial pattern of a preferential flow network over a large scale (30 m). We explore the feasibility of extending small-scale dye staining techniques to the hillslope scale. We determine the lateral preferential flow features that are active during the steady state flow conditions and their interaction with the surrounding soil matrix. We also calculate the velocities of the flow through each cross-section of the hillslope and compare them to hillslope scale applied tracer measurements. Finally, we investigate the relationship between the contributing area and the characteristics of the preferential features. The experiment revealed that larger contributing areas coincided with highly developed and hydraulically connected preferential features that had flow with little interaction with the surrounding soil matrix. We found evidence of subsurface erosion and deposition of soil and organic material laterally and vertically within the soil. These results are important because they add to the understanding of the runoff generation, solute transport, and slope stability of these types of hillslopes.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".