Characterization of ice coverage in the St. Lawrence River using satellite imagery and an operational ice status index
Bibliographic record
Abstract
The St. Lawrence River is part of an important international shipping route, with numerous shoreline communities and major infrastructure such as dams and hydroelectric power generation stations. In winter, ice is a crucial consideration in water management decisions on the upper part of the St. Lawrence, from Lake Ontario to Montreal. The water management board and hydroelectric companies closely monitor river ice conditions at two key locations, Lake St. Lawrence and Beauharnois Canal. River flow is usually reduced to encourage the formation of a stable ice cover and prevent problematic ice jams; once this cover forms, flow may be increased. Since the year 2000, detailed observations have been made on ice cover presence and stability, and a numeric ice status index has been recorded for each day of the ice season. The availability of remotely-sensed data, such as satellite imagery, affords another way to monitor ice conditions. Satellite-based optical imagery was used to classify pixels on the St. Lawrence River as either ice or water. The percent coverage of the surface by ice was then calculated over each area for images from 2013 to 2022. This satellite-derived ice coverage was compared with the ice status indicator timeseries, and preliminary correlations were established. Several machine-learning methods for synthetic aperture radar (SAR) imagery analysis were also tested and summarized.
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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 | 0.002 |
| 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) | 0.000 | 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".