A cold  cavity? Results of a high resolution ice-shelf ocean coupled model of Terra Nova Bay and the ocean cavity beneath the Nansen Ice Shelf.
Bibliographic record
Abstract
Terra Nova Bay in the western Ross Sea of Antactica has received increasing attention recently by international oceanographic and sea ice observation campaigns. In Terra Nova Bay strong katabatic events create one of the most intense sea ice producing polynyas in Antarctica. The associated deep convection drives the formation of HSSW, the precursor of AABW. It also facilitates the oceanic heat exchange with the adjacent ocean cavity beneath the Nansen Ice Shelf (NIS). Terra Nova Bay presents us with the unique opportunity of studying many of the primary interactive processes of atmosphere, ocean, ice shelves and sea ice, in a relatively confined region. In this talk we will show results of a high resolution, coupled ocean-ice shelf modeling study that synthesizes and contextualizes available data sets from various recent observation campaigns. Our results include the first tidal model of Terra Nova Bay and the NIS cavity, the seasonal heat budget of the cavity and the formation of meso-scale eddies inside the polynya. We have also investigated the oceanographic role of erosion features at the base of the NIS, associated ice shelf melt rates and the impact of fresh water outflow in preconditioning the onset of winter polynya activity as well as the large scale circulation in Terra Nova Bay.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".