High Resolution RADARSAT-2 SAR Data for Sea-Ice Classification in the Neighborhood of Nunavik's Marine Infrastructures
Bibliographic record
Abstract
Marine infrastructures are a key component for arctic communities. During the past decades, climate change effects have been observed throughout the Arctic and may be linked with marine infrastructure physical deterioration. Changes in the wind and water regimes and in the ice conditions are major factors explaining this observed phenomenon. Satellite radar images are often used to monitor sea ice conditions on a large scale. This study focuses on the use of high resolution radar images to assess the ice conditions during the freeze-up and break-up periods of 2009-2011 near the marine infrastructures of villages in Nunavik: Quaqtaq and Umiujaq. The data used in this study are RADARSAT-2 fine (9m) and ultra-fine (3m) images. They were processed using the Multivariate Iterative Region Growing using Semantics (MIRGS) algorithm developed in the Department of Systems Engineering at the University of Waterloo. Using MIRGS, sea ice maps are generated for the immediate neighbourhood of the marine infrastructures. Validation is made using air photos and ground photos taken at the infrastructures. Spatial statistics such as first ice appearance and different concentration thresholds are calculated for various buffers (0.1 to 10 km) around the infrastructure using spatial analysis methods in ArcGIS. The study is part of a larger project assessing the vulnerability of Nunavik’s marine infrastructures to climate change, led by Transport Quebec and the Ouranos Consortium. The ice maps and statistics will be used to document ice behaviour near the infrastructures and to validate a three-dimensional (3D) oceanic sea ice model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".