Satellite Monitoring of First Year Sea Ice Decay
Bibliographic record
Abstract
The Canadian Ice Service (CIS), a branch of the Meteorological Service of Canada (MSC), is mandated to continually monitor ice conditions in Canadian coastal areas in order to support ship navigation and other marine activities in waters where ice is present. New initiatives within the CIS require the development of techniques whereby the state of Arctic first year sea ice melt can be accurately assessed by those satellite-borne sensors used operationally by the CIS, primarily RADARSAT-1 and the Advanced Very High Resolution Radiometers. The seasonal decay of sea ice causes and is caused by physical changes in the ice volume. These changes ultimately result in the sea ice losing mechanical strength (Johnston et al., 2001), thus easing ship navigation, but increasing risk to those working or traveling on first year ice. Importantly, the sea ice volume’s optical, thermal and electrical properties are modified significantly by melt-related physical changes. As a result, the appearance of first year sea ice, in both AVHRR and RADARSAT-1 data, changes with the onset of melt conditions in the Arctic. With the help of other sea ice scientists, CIS is trying to characterize these signature changes and relate them to ice strength (Johnston et al., 2001). The end result of this work should be the capability of CIS to routinely assess ice decay from AVHRR and RADARSAT- 1 data. This information will ultimately be used to help support the new Arctic Ice Regime Shipping System (AIRSS) (AIRSS, 1996).
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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 | 0.002 | 0.002 |
| 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".