Modeling of Volitional Passage for the Big Bar Landslide Recovery
Bibliographic record
Abstract
The Big Bar landslide occurred on a remote section of the Fraser River, 64 km north of Lillooet, British Columbia, and created a barrier to the vital seasonal northward Fraser salmon migration. Intensive efforts were made by Canada Department of Fish and Oceans in 2019 as an emergency response to stabilize the slide source and to remove rockfall debris from the river. In 2020, Kiewit was awarded a contract to undertake additional remediation of the landslide and final design of a fishway structure and a volitional passage. Due to continuing rockfall through the winter of 2020–2021 the construction effort was limited to the volitional embankment with a temporary slope extension taking the place of the proposed fishway structure. The basis of the design used a combination of the USBR’s SRH-2D two-dimensional depth-averaged computations and grading design with the objective of creating a uniform gradient with an ample corridor for volitional passage with a mean velocity of less than 2.0 m/s. Given the use of large size riprap (1.5 m characteristic size) and the shallow flow depths associated with volitional pathway, the determination of velocity utilized research by Blodgett at the USGS to develop depth-varying boundary roughness relationships. The design process led to a unique grading configuration for the embankment. While considered a temporary configuration, the grading design worked successfully through the spring and summer freshet of 2021 permitting the unimpeded passage of chinook and sockeye salmon with little use of alternative transport means.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".