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Record W2153574897 · doi:10.3189/172756411797252103

Iceberg-capsize tsunamigenesis

2011· article· en· W2153574897 on OpenAlexfundno aff
Douglas R. MacAyeal, Dorian S. Abbot, O. V. Sergienko

Bibliographic record

VenueAnnals of Glaciology · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersCanadian Institute for Advanced ResearchUniversity of ChicagoNational Science Foundation
KeywordsIcebergGeologyIce calvingGlacierIce shelfSeismologyClimatologyOceanographyGeomorphologySea iceCryosphere

Abstract

fetched live from OpenAlex

Abstract Calving from the floating termini of outlet glaciers and ice shelves is just the beginning of an interesting chain of events that can subsequently have important impacts on human life and property. Immediately after calving, many icebergs capsize (roll over by 90°) due to the instability of their initial geometry. As icebergs melt and respond to the cumulative effects of ocean swell, they can also reorient their mass distribution by further capsize and fragmentation. These processes release gravitational potential energy and can produce impulsive large-amplitude surface-gravity waves known as tsunamis (a term derived from the Japanese language). Iceberg-capsize tsunamis in Greenland fjords can be of sufficient amplitude to threaten human life and cause destruction of property in settlements. Iceberg-capsize tsunamis may also have a role in determining why some ice shelves along the Antarctic Peninsula disintegrate ‘explosively’ in response to general environmental warming. To quantify iceberg tsunami hazards we investigate iceberg-capsize energetics, and develop a rule relating tsunami height to iceberg thickness. This rule suggests that the open-water tsunami height (located far from the iceberg and from shorelines where the height can be amplified) has an upper limit of 0.01 H where H is the initial vertical dimension of the iceberg.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.995

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.144
GPT teacher head0.275
Teacher spread0.131 · 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.

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

Citations57
Published2011
Admission routes1
Has abstractyes

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