Modelling freeze-up ice covers along the Exploits River, Newfoundland
Bibliographic record
Abstract
Although spring breakup ice jams have received more attention than freeze-up ice jams in the literature, perhaps due to their greater flood severity, freeze-up ice jams can still be a concern for flooding and pose highwater threats to riverside communities. This paper explores a methodology to model key processes of freeze-up ice jam flood hazard. These processes, modelled in the river ice hydraulic model RIVICE, are addressed using freeze-up along the Exploits River as a case study where the town of Badger is of most concern regarding ice-jam flood risk. Differences between simulated and observed extents of the ice cover and water level elevations served as objective functions for the calibration process, using the calibration of the heat transfer coefficient as an example. The calibration of the hydrological model HEC-HMS set up for the lower subbasin of the Exploits River drainage area is also introduced. The coupling of these two models will be used in future work for flood forecasting and climate change predictions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".