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Record W3209184767 · doi:10.26748/ksoe.2021.060

Review of Ice Characteristics in Ship-Iceberg Collisions

2021· article· en· W3209184767 on OpenAlexaboutno aff
Tak-Kee Lee, Hyun-Jin Park

Bibliographic record

VenueJournal of Ocean Engineering and Technology · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsIcebergSea iceOceanographyArctic ice packArcticGeologyAntarctic sea iceSeabed gouging by iceGlacierDrift iceIce shelfPhysical geographyGeographyClimatologyCryosphere

Abstract

fetched live from OpenAlex

The International Ice Patrol (IIP) was established after the Titanic collided with an iceberg off the eastern coast of Canada in 1912 and sank, killing more than 1,500 people. Recently, the IIP has analyzed satellite images and provided safe operation information to vessels by tracking the occurrence and movement of icebergs. A large number of recent arctic studies mainly deal with sea ice formed by freezing seawater related to sea routes and resource development. The iceberg that collided with the Titanic was land-based ice that dislodged from a glacier and fell into the sea. The properties of these two types of ice are different. In addition, vessels operating in ice-covered waters such as the Arctic sea have an ice-breaking function or minimum ice-strengthened functions. Ships operating on transatlantic routes including the eastern coast of Canada do not necessarily require ice-strengthened functions. Hundreds to thousands of icebergs are discovered each year near the area where the Titanic sank. In this study, the status of ship-iceberg collision accidents was investigated to provide useful information to researchers, and the physical and mechanical characteristics of icebergs were investigated and summarized.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.006
GPT teacher head0.198
Teacher spread0.192 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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