Evaluating the impact of fluvial geomorphology on river ice cover formation based on a global sensitivity analysis of a river ice model
Bibliographic record
Abstract
In 2011, Manitoba was stricken by wide-scale flooding causing high flows along the Dauphin River. The unprecedented high discharges at freeze-up created the potential for excessive ice cover thickening and backwater staging exacerbating the flood risk already threatening the communities along the river and upstream-lying Lake St. Martin. Hence, the river ice model RIVICE was implemented to determine flood protection elevations to which existing dikes needed to be raised and extended. Two reaches of the river were modelled separately representing distinct geomorphological characteristics: mildly sloping, more sinuous upper reach and steeper, more channelized lower reach. A global sensitivity analysis based on a Monte Carlo analysis was carried out to determine how differences in these morphological features influence different processes of the ice cover formation. It was found that the sinuous and braided morphology of the upper reach has a marked impact on the sensitivity of the hydraulic roughness and strength parameters. The structure of the ice (porosity) and the discharge were most sensitive to the backwater level outcomes of the steeper and straighter lower reach.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".