Application of CRISSP-2D finite element modelling in predicting ice formation processes upstream of the Jenpeg Generating Station
Bibliographic record
Abstract
The purpose of this study is to develop, evaluate, and apply a CRISSP-2D river ice model for a highly complex reach of the Nelson River upstream of the Jenpeg Generating Station in northern Manitoba. The calibrated model is applied in a backcasting scenario to evaluate its potential of predicting the river ice regime associated with specific hydraulic and meteorologic conditions. Secondly, a real-time application is conducted in collaboration with Manitoba Hydro to forecast overnight ice conditions as part of the 2011 Ice Stabilization Program. The model is shown to be fully capable of predicting the onset and type of ice regime that occurs. Spatial variation in ice generation across the study region is accurately captured, including locations of thermal bridging and initial ice front advance. Several modelling limitations associated with parameterization limit model accuracy during the latter stages of freeze-up and are identified as enhancement opportunities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".