Winter growth and tidal variability of the sub-ice platelet layer observed with electromagnetic induction soundings
Bibliographic record
Abstract
Abstract. Here, we present the first electromagnetic induction time-series measurements of ice shelf-influenced fast ice and sub-ice platelet layer thickness over winter and in late spring in McMurdo Sound. Significant increases in sub-ice platelet layer thickness (~0.5–1 m) co-occurred with strong southerly wind events and satellite-observed polynya activity suggesting wind-driven surface circulation of supercooled Ice Shelf Water outflow from the McMurdo-Ross ice shelf cavity. Temporal variability observed in sub-ice platelet layer thickness on diurnal timescales correlated with tidally-induced current patterns previously observed in McMurdo Sound. The thickness of the sub-ice platelet layer increased on spring and neap ebb tides corresponding with northward currents circulating out from the ice shelf cavity. The late spring spatial distributions of first-year and second-year fast ice and sub-ice platelet layer thickness in McMurdo Sound were assessed with drill hole and electromagnetic induction surveys and were comparable to a previous four-year dataset. We resolved second-year fast ice thicknesses of 4 m with a substantial sub-ice platelet layer beneath of up to 11 m using electromagnetic induction techniques suggesting that the longer temporal persistence of the two-year-old fast ice allowed a substantially thicker sub-ice platelet layer to form. The variability observed in the sub-ice platelet layer indicates that a combination of the tides, wind-driven polynya activity and the presence of multi-year ice influences the circulation of Ice Shelf Water in the upper surface ocean and consequently sub-ice platelet layer formation over a range of timescales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".