Extreme ice features distribution in the Canadian Arctic
Bibliographic record
Abstract
Extreme ice features (EIFs) present one of the key ice hazards affecting the design of structures in the Canadian and Alaskan Beaufort Seas. These features include ice islands that have calved from the ice shelves of Ellesmere Island and multi-year hummock fields (MYHFs), very large and thick features formed from multiple ridging events at the outer edge of the landfast ice and solidified over years. EIFs sometimes drift into offshore lease areas of the Southern Beaufort Sea. In 2008, a joint industry-government project was initiated to acquire extensive satellite imagery in a 100 km wide swath of the coastal corridor from the ice shelves of Ellesmere Island to Prince Patrick Island, in order to count and measure EIFs. In August 2008 there were major calvings from several ice shelves. The program captured a significant portion of the newly calved ice islands next to their parent ice shelves as well as older ice islands farther south. The imagery also captured the break-up of landfast ice and new MYHFs that resulted, as well as older drifting features. The paper describes the satellite imagery types and coverage. Most of the EIFs were detected from Envisat images of 30m resolution. The dimensions were measured and stored in a data base. A total of 200 EIFs were identified including 40 ice islands, 93 ice island fragments and 67 multi-year hummock fields. (An ice island fragment is less than 1 km in the longest dimension). The paper describes the data base and summarizes the statistics of equivalent diameter of EIFs. For ice islands the mean and maximum equivalent diameters were 1.6 and 5.2 km. For MYHFs the corresponding sizes were 1.7 km and 13.8 km. Comparisons are made with similar data bases from the 1990s and 1980s.
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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 | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".