How Ice Mapping Can Help Manage and Prevent Ice Jams: Remote Sensing Monitoring of the Saint-François River, Québec
Bibliographic record
Abstract
We focus on the Saint-François River (Quebec), which is known for recurrent ice jam-induced floods. This study addresses monitoring deficiencies and proposes solutions by presenting a comprehensive large-scale ice cover monitoring approach using diverse remote sensing tools for managing ice-jam risks effectively on this watercourse. We achieved three sub-objectives: (1) gathering spatial characteristics of the ice jam by acquiring images during the ice jam with an RGB camera-equipped drone; (2) mapping river ice using radar and optical images; and (3) river segmentation based on the dominant ice process. The methodological approach integrates data remotely sensed before and during the ice jam event, employing various tools. By comparing remote sensing methods with traditional monitoring, we underscore the importance of spatial data acquisition in ice-jam risk management. Orthomosaic and summary maps illustrate ice evolution processes, highlighting remote sensing efficacy in discerning hydro-meteorological events and emphasizing the need to target specific areas for risk mitigation. River segmentation based on the dominant ice process provides insights into freeze and thaw sequences, thereby illustrating ice evolution processes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".