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Record W4410846383 · doi:10.1080/17538947.2025.2511289

Fragmentation patterns of Antarctic icebergs in sea ice: observations and statistical data

2025· article· en· W4410846383 on OpenAlexaff
Zhenfu Guan, Yan Liu, Xiao Cheng, Teng Li, Mohammed Shokr, Xuying Liu, Shaoyin Wang, Lei Zheng, Zilong Chen

Bibliographic record

VenueInternational Journal of Digital Earth · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Natural Science Foundation of China
KeywordsIcebergFragmentation (computing)Sea iceGeographyGeologyPhysical geographyCartographyOceanographyComputer science

Abstract

fetched live from OpenAlex

Fragmentation is a key process in Antarctic iceberg decay, influencing the Antarctic climate and ecosystems. However, iceberg fragmentation has not been quantified at the pan-Antarctic scale. Using Sentinel-1 data from August to October 2019, we identified 407 fragmentation events in the circum-Antarctic near-coastal zone, with original iceberg sizes ranging from 0.01 km² to 5591.34 km². The Indian Ocean sector had the greatest number of fragmented events, 97% of which involved icebergs less than 1 km², whereas the Bellingshausen–Amundsen Sea sector has experienced the highest number of fragmentation events involving medium to large icebergs. Smaller icebergs (less than 1 km²) were more susceptible to disintegration through highly fractured capsizing, whereas larger icebergs underwent disarticulation. Fragmentation events were less frequent in landfast ice or mélange (∼0.5% monthly), whereas icebergs exceeding 10 km² exhibited a notable increase in the ratio of fragmentation events to the total number of icebergs (more than 15% monthly) when they were in motion and rotating in pack ice. Our findings indicate that during winter and under extensive sea ice cover, internal ocean waves, ocean currents and collisions are key factors influencing iceberg fragmentation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.027
Threshold uncertainty score0.198

Codex and Gemma teacher scores by category

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.288
Teacher spread0.250 · 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 teacher head, 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

Citations2
Published2025
Admission routes1
Has abstractyes

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