Exploring the Impact of Various Streamflow Products on Ice-Jam Formation 
Bibliographic record
Abstract
Ice jams present a significant flood risk in communities located along northern rivers, especially during the breakup of ice cover, resulting in increased backwater levels and flooding beyond riverbanks. The accurate simulation of ice formation relies on precise streamflow data, a vital input for hydraulic models. This study aims to evaluate how different streamflow products influence ice formation, focusing on simulating ice-jam flooding of the Athabasca River at Fort McMurray, Canada, with the broader goal of assessing the suitability of global datasets for predicting such events at a local scale. In our investigation, we integrate MizuRoute, a river network routing tool, and RIVICE, a one-dimensional, hydrodynamic, and river-ice hydraulic model. By employing various large-scale runoff from different models and datasets, such as MESH, ERA5, and VIC among others, our goal is to comprehensively understand how each product impacts the formation of ice jams and the subsequent flooding events. The incorporation of these runoff products is particularly relevant to investigate utilizing global datasets for predicting ice-jam flooding at a local scale.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".