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Record W4360850681 · doi:10.1017/aog.2023.5

The role of channelized basal melt in ice-shelf stability: recent progress and future priorities

2022· article· en· W4360850681 on OpenAlexafffund
Karen E. Alley, T. A. Scambos, Richard B. Alley

Bibliographic record

VenueAnnals of Glaciology · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Manitoba
FundersNatural Environment Research CouncilNuclear Safety and Security CommissionNatural Sciences and Engineering Research Council of CanadaU.S. Geological SurveyNational Aeronautics and Space AdministrationHeising-Simons FoundationNational Science Foundation
KeywordsChannelizedGeologyMeltwaterIce shelfOceanographyBasal (medicine)Lead (geology)Channel (broadcasting)IcebergContinental shelfIce sheetSea iceGeomorphologyGlacial periodCryosphereBiology

Abstract

fetched live from OpenAlex

Abstract Basal channels, which form where buoyant plumes of ocean water and meltwater carve troughs upwards into ice-shelf bases, are widespread on Antarctic ice shelves. The formation of these features modulates ice-shelf basal melt by influencing the flow of buoyant plumes, and influences structural stability through concentration of strain and interactions with fractures. Because of these effects, and because basal channels can change rapidly, on timescales similar to those of ice-shelf evolution, constraining the impacts of basal channels on ice shelves is necessary for predicting future ice-shelf destabilization and retreat. We suggest that future research priorities should include constraining patterns and rates of basal channel change, determining mechanisms and detailed patterns of basal melt, and quantifying the influence that channel-related fractures have on ice-shelf stability.

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)0.008
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.003
Science and technology studies0.0010.004
Scholarly communication0.0040.006
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.037
GPT teacher head0.261
Teacher spread0.224 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations31
Published2022
Admission routes2
Has abstractyes

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