Investigation and modelling of anchor ice formation and release processes at Clark Lake
Bibliographic record
Abstract
Anchor ice impacts are commonly observed at the outlet of Clark Lake, which is in northern Manitoba upstream of the Keeyask Generating Station (GS) operated by Manitoba Hydro. Manitoba Hydro experiences challenges in accurately forecasting the inflow reaching the Keeyask GS during winter months due to the variable formation and release of anchor ice at the outlet of Clark Lake, which can result in potential revenue losses. This work improves the understanding of anchor ice and other ice processes at this location by performing historical analysis and conducting an on-site field monitoring program, and provides an empirical model to predict the timing of anchor ice intended for Manitoba Hydro to use in operation. There were 88 definite ice events identified over the 2003/04-2022/23 winter seasons at the outlet of Clark Lake, with 81 being anchor ice. Analysis of the ice events identified proved that different types of ice events occurred, and they were further divided into categories based on their dominant ice type, anchor ice duration type, timing, and release type. There were general trends found between different ice event categories related to their timing, size, and duration. The energy budget trends for the largest formation and release events in both the field monitoring program winters and the historical winters were investigated in more detail. It was found that the energy budget typically decreased surrounding major formation events and increased surrounding major release events, with the sensible heat flux being the dominant heat flux. Dynamic threshold models were developed to predict anchor ice formation and release events, independently, using the change in the sensible and evaporative heat flux as the predictor variables. The final threshold models developed had a 73% and 72% weekly accuracy, for formation and release, respectively, with a higher accuracy for major events. Future work should focus on developing a model to predict the magnitude and duration of the ice impacts in addition to timing.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".