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Record W6910397779 · doi:10.4224/12341004

Ice-induced global loads on USCGC Healy and CCGS Louis S. St-Laurent as determined from whole-ship motions

2003· report· en· W6910397779 on OpenAlexaffvenue

Bibliographic record

VenueNPARC · 2003
Typereport
Languageen
Field
Topic
Canadian institutionsNational Research Council CanadaCanadian Wood Council
Fundersnot available
KeywordsSea iceGlobal temperatureIce capsIce field

Abstract

fetched live from OpenAlex

This report provides documentation on the recently updated MOTAN system. The MOTAN uses measured whole-ship motions to obtain the global loads associated with transient ice impacts. Model-scale and full-scale data are used to demonstrate the feasibility of using the MOTAN system to provide reliable information on global loads. Whole-ship motions of the icebreakers HEALY and LOUIS, which have similar ship displacements, are used to determine ice-induced global loads for more than 200 impacts. Results show good agreement between global loads measured on the HEALY and LOUIS. Impacts with warm first-year ice at ship speeds up to 11 kt caused global loads from 1.7 to 7.0 MN. Impacts with medium to thick second-year and multi-year ice at speeds from 0.6 to 16.1 kt resulted in global loads from 1.9 to 17.3 MN. The three highest global loads on the LOUIS (15.7, 16.7 and 17.3 MN) resulted from ramming a rubbled multi-year floe at about 11 kt. The three impacts occurred in quick succession, over a period of about 5 seconds, as the ship slowed to 5 kt from 11 kt. Plotting the initial impact force (the first in a series) versus ship speed showed a clear trend of increasing global load with increasing ship speed, for the multi-year ice events. A lesser correlation was found for thick second-year ice impacts and no apparent correlation for thick first-year and medium second-year ice impacts.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.521
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.006

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 head0.060
GPT teacher head0.327
Teacher spread0.267 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations4
Published2003
Admission routes2
Has abstractyes

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