First “in situ” determination of gas transport coefficients (, , and ) from bulk gas concentration measurements (O<sub>2</sub>, N<sub>2</sub>, Ar) in natural sea ice
Bibliographic record
Abstract
Abstract We report bulk gas concentrations of O 2 , N 2 , and Ar, as well as their transport coefficients, in natural landfast subarctic sea ice in southwest Greenland. The observed bulk ice gas composition was 27.5% O 2 , 71.4% N 2 , and 1.09% Ar. Most previous studies suggest that convective transport is the main driver of gas displacement in sea ice and have neglected diffusion processes. According to our data, brines were stratified within the ice, so that no convective transport could occur within the brine system. Therefore, diffusive transport was the main driver of gas migration. By analyzing the temporal evolution of an internal gas peak within the ice, we deduced the bulk gas transport coefficients for oxygen (D O2 ), argon (D Ar ), and nitrogen (D N2 ). The values fit to the few existing estimates from experimental work, and are close to the diffusivity values in water (10 −5 cm 2 s −1 ). We suggest that gas bubbles escaping from the brine to the atmosphere—as the ice gets more permeable during melt—could be responsible for the previously reported high transport coefficients. These results underline that when there is no convective transport within the sea ice, the transport of gas by diffusion through the brines, either in the liquid or gaseous phases, is a major factor in controlling the ocean‐atmosphere exchange.
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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 | 0.000 | 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".