Bibliographic record
Abstract
Abstract. The effect that sea ice topography has on the momentum transfer between ice and atmosphere is not fully quantified due to the vast extent of the Arctic and limitations of current measurement techniques. Here we present a method to estimate pan-Arctic momentum transfer via a parameterization which links sea ice–atmosphere form drag coefficients with surface feature height and spacing. We measure these sea ice surface feature parameters using the Cloud and land Elevation Satellite-2 (ICESat-2) which, though it cannot resolve as well airborne surveys, has a higher along-track spatial resolution than other contemporary altimeter satellites. As some narrow obstacles are effectively smoothed out by the ICESat-2 ATL07 spatial resolution, we use near-coincident high-resolution Airborne Topographic Mapper (ATM) elevation data from NASA's Operation IceBridge (OIB) mission to scale up the regional ICESat-2 drag estimates. By also incorporating drag due to open water, floe edges and sea ice skin drag, we produced a time series of average total pan-Arctic neutral atmospheric drag coefficient estimates from October 2018 to May 2022. Here we have observed its temporal evolution to be unique and not directly tied to sea ice extent. By also mapping 3-month aggregates for the years 2019, 2020 and 2021 for better regional analysis, we found the thick multiyear ice area directly north of the Canadian Archipelago and Greenland to be consistently above 2.0 · 10−3 with rough ice ∼ 1.5 · 10−3 typically filling the full multiyear ice portion of the Arctic each spring.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.027 | 0.029 |
| Insufficient payload (model declined to judge) | 0.083 | 0.067 |
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".