Characterization of ice retention during breakup upstream of an ice control structure using a juxtaposed camera system
Bibliographic record
Abstract
AbstractIce Control Structures (ICS) are instream structures that were built to reduce breakup ice jams flooding by intercepting ice blocks upstream of riverine communities. With the imposing effects of climate change, some of these ICSs were found to be less efficient and sometimes more problematic, showcasing the necessity of rebuilding or optimization of existing configuration. Among these two options, optimization is likely the most common due to the expensive costs related to a new ICS. To optimize the performance of an existing ICS, first the interaction between the ICS and ice blocks (current performance and ice pieces physical aspect) should be documented and analyzed. For this project, a camera-based system was employed on Sartigan dam, an ICS in Chaudière River during the 2023 breakup season to understand and characterize the retention of ice upstream of the structure. A total of 18 ice breakup events were identified. The results show that during 11 events (61% of the time) the structure was able to hold the ice run in place. Ice blocks interacting with the ICS had an average cord length of 1.13 m, and moving toward the structure with an average speed of 0.41 m/s. During the event with holding time, ice blocks were moving in the accumulation with an average consolidation speed of 0.023 m/s.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".