Influence of Debris Jam Formed by Trees on Bridge Pier Scour
Bibliographic record
Abstract
Debris jams contribute to bridge pier failures. Previous investigations showed that a debris jam can constrict the flow cross section, enhance flow intensity, and result in significant scour. Physical modeling was conducted to investigate the influence of debris jams on scour depth. Instead of using a static block to represent a debris jam, dynamic debris jams composed of real tree seedlings were investigated to represent jams forming of woody debris with roots and branches. The dynamic jam was achieved by continuously releasing individual seedlings from upstream. The resulting seedling jams had a typical half-cone shape and generally grew continuously over time, with observed scour increasing with the size of the debris jam. The scour in the presence of a dynamic debris jam could have depth up to twice and volume up to eight times that of a pier without a debris jam. In addition, the dynamic debris jam also induced additional hydraulic head across the cylinder pier, which correlated with the size of debris jam and Froude number.
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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".