Airborne Observations of Summer Thinning of Multiyear Sea Ice Originating From the Lincoln Sea
Bibliographic record
Abstract
Abstract To better understand recent changes of Arctic sea ice thickness and extent, it is important to distinguish between the contributions of winter growth and summer melt to the sea ice mass balance. In this study we present a Lagrangian approach to quantify summer sea ice melt in which multiyear ice (MYI) floes that were surveyed by airborne electromagnetic thickness sounding within Nares Strait during summer were backtracked, using satellite imagery, to a region in close proximity (3–20 km) to spring ice thickness surveys carried out in the Lincoln Sea. Typical modal total MYI thicknesses, including ~0.4‐m snow, ranged between 3.9 and 4.7 m in the Lincoln Sea during April. Ice‐only modal thicknesses were between 2.2 and 3.0 m in Nares Strait during August. Total thinning including snow and ice was 1.3 ± 0.1 m including 0.4 ± 0.09 m of snow melt and 0.9 ± 0.2 m of ice melt. This translates to a seasonal net heat input of 305 ± 69 MJ/m2 (262 ± 60 MJ/m2 for ice only) and seasonal net heat flux of 57 ± 13 W/m2 (45 ± 10 W/m2 for ice only), which is unlikely to be explained by solar radiation fluxes alone. Furthermore, our approach provides an improvement on traditional ice mass balance buoy estimates because it integrates melt over larger spatial scales, where melt can be highly variable due to differential melt experienced between melt ponds, bare ice, hummocks, and ridges.
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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 | 0.000 |
| 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) | 0.001 | 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".