How well does wind speed predict air‐sea gas transfer in the sea ice zone? A synthesis of radon deficit profiles in the upper water column of the <scp>A</scp>rctic <scp>O</scp>cean
Bibliographic record
Abstract
Abstract We present 34 profiles of radon‐deficit from the ice‐ocean boundary layer of the Beaufort Sea. Including these 34, there are presently 58 published radon‐deficit estimates of air‐sea gas transfer velocity ( k ) in the Arctic Ocean; 52 of these estimates were derived from water covered by 10% sea ice or more. The average value of k collected since 2011 is 4.0 ± 1.2 m d −1 . This exceeds the quadratic wind speed prediction of weighted k ws = 2.85 m d −1 with mean‐weighted wind speed of 6.4 m s −1 . We show how ice cover changes the mixed‐layer radon budget, and yields an “effective gas transfer velocity.” We use these 58 estimates to statistically evaluate the suitability of a wind speed parameterization for k , when the ocean surface is ice covered. Whereas the six profiles taken from the open ocean indicate a statistically good fit to wind speed parameterizations, the same parameterizations could not reproduce k from the sea ice zone. We conclude that techniques for estimating k in the open ocean cannot be similarly applied to determine k in the presence of sea ice. The magnitude of k through gaps in the ice may reach high values as ice cover increases, possibly as a result of focused turbulence dissipation at openings in the free surface. These 58 profiles are presently the most complete set of estimates of k across seasons and variable ice cover; as dissolved tracer budgets they reflect air‐sea gas exchange with no impact from air‐ice gas exchange.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".