Development of an Ice Jam Flood Forecasting System for the Lower Oder River—Requirements for Real-Time Predictions of Water, Ice and Sediment Transport
Bibliographic record
Abstract
Despite ubiquitous warming, the lower Oder River typically freezes over almost every year. Ice jams may occur during freeze-up and ice cover breakup phases, particularly in the middle and lower reaches of the river, with weirs and piers. The slush ice and ice blocks may accumulate to form ice jams, leading to backwater effects and substantial water level rise. The small bottom slope of the lower Oder and the tidal backflow from the Baltic Sea enhance the formation of ice jams during cold weather conditions, jeopardizing the dikes. Therefore, development of an ice jam flood forecasting system for the Oder River is much needed. This commentary presents selected results from an international workshop that took place in Wrocław (Poland) on 26–27 November 2018 that brought together an international team of experts to explore the requirements and research opportunities in the field of ice jam flood forecasting and risk assessment for the Oder River section along the German–Polish border. The workshop launched a platform for collaboration amongst Canadian, German and Polish scientists, government officials and water managers to pave a way forward for joint research focused on achieving the long-term goal of forecasting, assessing and mitigating ice jam impacts along the lower Oder. German and Polish government agencies are in need of new tools to forecast ice jams and assess their subsequent consequences and risks to communities and ship navigation along a river. Addressing these issues will also help research and ice flood management in a Canadian context. A research program would aim to develop a modelling system by addressing fundamental issues that impede the prediction of ice jam events and their consequences in cold regions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".