Comparison of Large Woody Debris Prototypes in a Large Scale Non-flume Physical Model
Bibliographic record
Abstract
Due to excessive rainfall in June of 2013, several rivers located in and near the City of Calgary, Canada experienced significant flooding events. These events caused severe damage to infrastructure throughout the city, precipitating a renewed interest in flood control and mitigation strategies for the area. A major potential strategy involves partial diversion of Elbow River flood water to the proposed Springbank Off-Stream Storage Reservoir. A large scale physical model study was conducted to optimize and validate the design of a portion of the new project. The goals of the physical model were to investigate diversion system behaviors such as flow rates, water levels, sediment transport and, debris accumulation, and optimize the design of new flow control structures to be constructed on the Elbow River. In order to accurately represent the behavior of debris within the system due to flooding, large woody debris created from natural sources was utilized in the physical model and its performance was compared to that of debris of the same size fabricated from pressed cylindrical wood dowels. In addition to comparing the performance of these two debris types, the impact of root wads on debris damming was also investigated. Significant differences in damming behavior was shown to exist between the natural debris and the fabricated debris, while the impact of root wad on damming affected the dam structure and formation. The results of this experiment indicate that natural debris is preferred for studies involving debris accumulation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".