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Record W4416301904 · doi:10.1029/2024jc021342

Ice Shelf Water‐Influenced Fast Ice and Sub‐Ice Platelet Layer Near the Campbell Ice Tongue, Terra Nova Bay

2025· article· en· W4416301904 on OpenAlexaff
Gemma Marie Brett, Natasha Blaize Gardiner, Patricia J. Langhorne, Wolfgang Rack, Christian Haas, Anne Irvin, Sanghee Kim

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsMemorial University of NewfoundlandYork UniversityUniversity of Alberta
Fundersnot available
KeywordsIce shelfSea iceCryosphereAntarctic sea iceArctic ice packIcebergIce sheetBay

Abstract

fetched live from OpenAlex

Abstract Here, we present the first dedicated in situ measurements of the thickness distributions of fast ice and the sub‐ice platelet layer, formed by supercooled Ice Shelf Water in north Terra Nova Bay, Antarctica. 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 indicating highly channeled outflow of supercooled Ice Shelf Water from beneath the ice tongue directed by 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, polynya forcing, and ocean circulation.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.036
GPT teacher head0.304
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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