The sub-ice platelet layer and its influence on freeboard to thickness conversion of Antarctic sea ice
Bibliographic record
Abstract
Abstract. This is an investigation to quantify the influence of the sub-ice platelet layer on satellite measurements of total freeboard and their conversion to thickness of Antarctic sea ice. The sub-ice platelet layer forms as a result of the seaward advection of supercooled ice shelf water from beneath ice shelves. This ice shelf water provides an oceanic heat sink promoting the formation of platelet crystals which accumulate at the sea ice–ocean interface. The build-up of this porous layer increases sea ice freeboard, and if not accounted for, leads to overestimates of sea ice thickness from surface elevation measurements. In order to quantify this buoyant effect, the solid fraction of the sub-ice platelet layer must be estimated. An extensive in situ data set measured in 2011 in McMurdo Sound in the southwestern Ross Sea is used to achieve this. We use drill-hole measurements and the hydrostatic equilibrium assumption to estimate a mean value for the solid fraction of this sub-ice platelet layer of 0.16. This is highly dependent upon the uncertainty in sea ice density. We test this value with independent Global Navigation Satellite System (GNSS) surface elevation data to estimate sea ice thickness. We find that sea ice thickness can be overestimated by up to 19%, with a mean deviation of 12% as a result of the influence of the sub-ice platelet layer. It is concluded that within 100 km of an ice shelf this influence might need to be considered when undertaking sea ice thickness investigations using remote sensing surface elevation measurements.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".