Brine Convection, Temperature Fluctuations, and Permeability in Winter Antarctic Land‐Fast Sea Ice
Bibliographic record
Abstract
Abstract Vertical temperature strings are used in sea ice research to study heat flow, ice growth rate, and ocean‐ice‐atmosphere interaction. We demonstrate the feasibility of using temperature fluctuations as a proxy for fluid movement, a key process for supplying nutrients to Antarctic sea ice algal communities. Four strings were deployed in growing, land‐fast sea ice in McMurdo Sound, Antarctica. By smoothing temperature data with the robust LOESS method, we obtain temperature fluctuations that cannot be explained by insolation or atmospheric heat loss. Statistical distributions of these temperature fluctuations are investigated with sensitivities to the distance from the ice‐ocean interface, average ice temperature, and sea ice structure. Fluctuations are greatest close to the base (<50 mm) at temperatures >−3°C, and are discrete events with an average active period of 43% compared to 11% when the ice is colder (–3°C to −5°C). Assuming fluctuations occur when the Rayleigh number, derived from mushy layer theory, exceeds a critical value of 10 we approximate the harmonic mean permeability of this thick (>1 m) sea ice in terms of distance from the ice‐ocean interface. Near the base, we obtain values in the same range as those measured by others in Arctic spring and summer. The permeability between the ice‐ocean interface and 0.05 ± 0.04 m above it is of order 10−9 m2. Columnar and incorporated platelet ice permeability distributions in the bottom 0.1 m of winter Antarctic sea ice are statistically significantly different although their arithmetic means are indistinguishable.
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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".