Flume investigation of landslide debris–resisting baffles
Bibliographic record
Abstract
Landslide debris is a common occurrence in mountainous regions around the world that can potentially result in disastrous consequences to downstream facilities. Flow-impeding structures are often constructed along the flow path to impede this hazardous phenomenon. Baffles are a type of flow-impeding structure regularly installed using empirical and prescriptive design methods as the interaction mechanism and the influence of baffle configuration on flow impedance is not well understood. A series of flume experiments were carried out to investigate flows characterizing landslide debris impacting an array of baffles using dry uniform sand. The influence of baffle height, row number, and spacing between successive rows was examined. Photoconductive sensors were used to estimate flow velocity, laser sensors were installed to measure flow depth profiles, and high-speed cameras were used to capture flow kinematics. Experimental results reveal that baffles can be categorized relative to the approach flow depth (h) and increasing the baffle height from 0.75h to 1.5h leads to a 40% increase in upstream flow depths from backwater effects, more effective development of subcritical conditions, and additional energy losses of up to 9%. Increasing the number of rows of 1.5h baffles from a single row to a three-row staggered array results in up to 72% additional energy loss. The energy loss is attributed to the deflection of granular jets and additional backwater effects. Increasing the row spacing from 50 to 100 mm results in up to a 14% increase in energy loss.
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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".