Numerical modeling of ice control on the Albany River, Ontario, Canada
Bibliographic record
Abstract
The Kashechewan First Nation (KFN) community is located on a floodplain of the north branch of the Albany River, on the west coast of James Bay. Each spring, the community faces the uncertain prospect of evacuation due to ice-jam flooding, the most severe of which occurred in 2006. A study was conducted to develop long-term options for reducing the flood risk to the community, which included dams, dykes, pier and weir type ice-control structures, and conveyance channels. This paper presents a numerical model study on the Albany River in Ontario, Canada to develop ice control as a potential means for mitigating ice-jam flooding at the KFN community using the two-dimensional ice dynamics model DynaRICE. A method was developed to simulate pier type ice-control structures within the model such that the physical channel conditions, flow conditions, and combinations of ice control could be evaluated. The model allowed detailed simulation of ice retention, resulting inundation, and the forces on the individual piers. To optimize the design of the piers, including pier spacing, height, and size, various combinations of ice, flow, and pier conditions were simulated. Numerical modeling facilitated a comparative examination of flood risk reduction to the community from the various ice control options.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".