Ice Shelf Water-influenced Fast Ice and Sub-Ice Platelet Layer Thickness Distributions beside the Campbell Ice Tongue in Terra Nova Bay, Antarctica
Bibliographic record
Abstract
Prior to the present study, no dedicated in situ measurement of the thickness distributions of fast ice and the sub-ice platelet layer, formed by supercooled Ice Shelf Water in the near-surface ocean, had been carried out in Terra Nova Bay. Previous studies have recognised the biological importance of the sub-ice platelet layer observed beneath fast ice beside the Campbell Ice Tongue. Furthermore, a recent airborne survey of fast ice in the western Ross Sea implied that smaller ice bodies (ice tongues and outlet glaciers) contribute to the formation of supercooled Ice Shelf Water. With the objective of inferring source regions and circulation of Ice Shelf Water, we measured fast ice and sub-ice platelet layer thickness distributions near the Campbell Ice Tongue in late spring of 2021, using drill hole surveys and high-resolution ground-based electromagnetic induction soundings. We observed thicker fast ice and sub-ice platelet layer near the ice tongue with very thick and narrow sub-ice platelet layer maxima identifying highly channelled outflow of supercooled Ice Shelf Water from beneath the ice tongue through ice mélange, subglacial formations, and grounded regions. We conclude that a significant volume of supercooled Ice Shelf Water is locally sourced from the Campbell Ice Tongue through basal melting and affirm that the icescape in north Terra Nova Bay results from a complex interplay of glacial morphology, bathymetry, polynya dynamics, and ocean circulation.
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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 | 0.001 |
| 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".