The Inferred Formation of a Subice Platelet Layer Below the Multiyear Landfast Sea Ice in the Wandel Sea (NE Greenland) Induced by Meltwater Drainage
Bibliographic record
Abstract
Abstract Oceanographic and ice‐mass‐balance records are presented from two moorings deployed on landfast multiyear ice in the Wandel Sea (North Greenland) during June–August 2015. Here we show that the melting and drainage of >1 m of snow from June 14 to July 14 created a double‐diffusive vertical stratification which resulted in supercooling of water and enabled the formation of platelet crystals below the sea ice. Although the effect of supercooling, with temperatures up to 0.5°C below the freezing point, might be overestimated considerably in our records, this process led to the formation of ∼1.1–1.2 m‐thick subice platelet layer. While warm water temperatures lead to the complete loss of this layer at one mooring site, the layer persisted through summer and became incorporated into the congelation ice at the second site. The warm water that melted out the platelet layer can be ascribed to two different sources: (1) in situ heating from solar radiation resulting in a temperature increase up to 0.8°C in late July and (2) advection of warm surface water (with temperatures up to 3–4°C) from the ice‐free coastal regions in mid‐August. The combination of processes causing the seasonal growth of a platelet layer and either its subsequent ablation or incorporation into congelation ice is discussed with respect to the ice‐mass balance and stability of the landfast ice cover in the Wandel Sea. Furthermore, this study provides evidence for the formation of a platelet layer in the Arctic, a phenomenon that historically has only been observed in the Antarctic.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".