Assessment of debris issues impacting design of a flood diversion project in a large scale physical model
Bibliographic record
Abstract
The Elbow River flows through southwest Calgary and is susceptible to flooding with catastrophic results. During the flood event of June 2013, the Elbow River’s peak flow rates reached approximately 1,240 m3/s, far exceeding the rivers’s natural capacity of less than 200 m3/s. The losses experienced during the flood were valued in excess of $5B. The Springbank Off-stream Storage Project was conceived to divert and store a portion of the Elbow River flood flows. Some of the main structural components of the project include a diversion and spillway structure that intersects the Elbow River, a diversion channel, and an off-stream storage reservoir. Following the development of an initial diversion and spillway structure design, a large scale (1:16) physical model study was commissioned to assist in assessing and improving the initial design to ensure good performance under a range of flood conditions. The main objectives of the physical model study were to: determine the hydraulic performance of various key elements of the new structures for a range of operational and extreme flow conditions; assess the behaviour of sediments and woody debris within and around the structures; and help refine the proposed designs to improve conveyance, reduce the risk of erosion and sedimentation, reduce the risk of blockage by debris, improve constructability, and reduce costs where possible. This paper provides a full description of the physical model with a focus on the tests that assessed and studied the impacts of woody debris.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".