Piezometric response in zones of a watershed with lateral preferential flow as a first‐order control on subsurface flow
Bibliographic record
Abstract
Abstract Characterizing zones of a watershed based on the water table is used to understand and predict internal watershed processes. In watersheds dominated by lateral preferential flow, the water table response typically shows a distinct hydraulically limited pattern. This response is characterized by a capping of the rising water table when the lateral preferential flow features are activated and subsurface flow still increases. We expected that this response would be related to the contributing area since nearby hillslope excavations showed that the development of preferential flow network was positively correlated with the contributing area. The watershed was stratified into three predetermined zones and installed 25 piezometers to measure the water table dynamics. The objectives were (1) to characterize the water table–runoff relationship, (2) to prove preferential flow by observable characteristics and (3) to test the feasibility of identifying areas within a watershed that are dominated by lateral preferential flow. Watershed zones were not well defined and there was no strong relationship between the hydraulically limited response and observable watershed characteristics. Although zones might still be useful for grouping the hillslope processes, the piezometric response may not be an appropriate indicator for mapping the watershed into areas with runoff dominated by lateral preferential flow. Copyright © 2010 John Wiley & Sons, Ltd.
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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) | 0.001 | 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".