Airborne Observations of the Distribution, Thickness, and Drift of Different Sea Ice Types and Extreme Ice Features in the Canadian Beaufort Sea
Bibliographic record
Abstract
Abstract Extensive airborne electromagnetic (EM) ice thickness surveys have beenperformed in April 2009, 2011, and 2012 over the Canadian Beaufort Sea with along-range airplane. These are contributing to the Beaufort RegionalEnvironmental Assessment (BREA) project which gathers ice information inpreparation of a regulatory framework for safe and environmental responsibleoil and gas production. Results show that the location of the multiyear iceedge can be very variable from year to year. Multiyear ice modal thicknessesranged between 3.0 and 3.7 m. The seasonal ice zone had very variable icethicknesses depending on the amount and age of ice formed in coastal polynyasand leads throughout the winter. However, we gathered enough data to show thatmodal first-year ice thicknesses of 2.0 to 2.2 m emerge if profiles are longenough, which can be considered the most representative first-year icethickness estimate in the Canadian Beaufort Sea in April. However, in theseasonal ice zone also regions with heavily deformed ice thicker than 10 m, andoccasional multiyear hummock fields of similar thicknesses occur. Resultssuggest that multiyear hummock fields may not comprise the thickest ice as theyare affected by melt during the summer. Two ice islands had thicknesses between20 and 30 m. Our results suggest a melt rate of ice islands of 10 m per year inthe Southern Beaufort Sea. Ice thickness surveys were complemented by theanalysis of satellite radar data and tracking of ice features by means of GPSbeacons. We demonstrate that all these activities combined comprise a powerfultool for a future Arctic sea ice environmental observatory.
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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 | 0.001 | 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".