Snow-ice accretion and snow-cover depletion on Antarctic first-year sea-ice floes
Bibliographic record
Abstract
Abstract Between austral late winter 1993 and austral autumn 1998, during five cruises aboard the research vessel Nathaniel B. Palmer, almost 300 m of core was obtained from first-year ice floes in the Ross, Amundsen and Bellingshausen Seas. Analysis of the texture, stratigraphy and stable-isotopic composition of the ice was used to assess the magnitude of the role of flooding and snow-ice formation at the base of the snowpack in the thickening of the ice cover and the thinning of the snow cover. Snow ice occurred in all ice-thickness categories and made a significant contribution to the total ice mass (12−36%) in both autumn and winter. Although the amount of snow ice was often exceeded by the amount of frazil ice and congelation ice, the thickness of individual layers of each ice type indicated that snow ice often made a greater contribution to the thermodynamic thickening of the ice cover than the other ice types. The larger quantities of frazil ice and congelation ice were primarily the result of dynamic thickening. Flooding and snow-ice formation reduced the snow cover to 42−70% of the total snow accumulation depending on time and location. On the basis of this information, ship-based snow-depth estimates were adjusted to estimate the total snow accumulation on different ice-thickness categories.
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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 | 0.001 |
| 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".