Coupled vertical and lateral preferential flow on a forested slope
Bibliographic record
Abstract
Coupling of vertical and lateral preferential flow paths was examined on a forested slope with thin soil cover during artificial irrigations. Point‐scale infiltration was measured at sites with differing soil macroporosities using vertical profiles of time domain reflectometry probes and suction samplers. Lateral fluxes of water and solutes from the slope were determined at a through flow trench. Sites with greater macroporosities tended to exhibit vertical preferential flow, while infiltration at sites with relatively small macroporosities was largely by vertical propagation of a well defined wetting front through the soil profile. Generation of vertical preferential flow at sites with relatively large macroporosities increased with input intensity. Lateral macropores made a minor contribution to slope runoff. Instead, runoff largely occurred in a thin saturated layer at the soil‐bedrock interface, both in a highly conductive zone at the bedrock surface and in the overlying saturated soil matrix. Some assumptions underlying the use of isotopic and geochemical tracers to study runoff generation are called into question by complex mixing of event and pre‐event water in this saturated layer. Soil depth, bedrock topography, and antecedent soil wetness determine the thickness, connectivity, and upslope extent of the pre‐event saturated layer above the bedrock surface. These, in turn, control whether vertical preferential and matrix flow reaching the bedrock surface participate in slope runoff.
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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".