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Record W2089535920 · doi:10.1179/030801804225018783

Building ships from ice: Habbakuk and after

2004· article· en· W2089535920 on OpenAlexaboutno aff
L. W. Gold

Bibliographic record

VenueInterdisciplinary Science Reviews · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Marine engineeringEngineeringCivil engineeringComputer scienceEnvironmental scienceAeronauticsOperations researchMechanical engineering

Abstract

fetched live from OpenAlex

AbstractA remarkable study was carried out during the Second World War on the feasibility of building large ships from ice. It received support at the highest political level because of the need for floating platforms to support aircraft operations. Extensive investigations were carried out on the mechanical properties of plain and reinforced ice and on the structural and operational characteristics of the proposed vessels. Investigations on methods for reinforcing ice led to the development of 'pykrete', a frozen mixture of water and wood pulp that was appreciably stronger and tougher than plain ice. The studies on the structural and operational characteristics of the vessels showed that the work required to design, construct and demonstrate the effectiveness and safety of the ships would be far greater than originally thought. This paper gives an abbreviated description of the work that was done, based on project files in Great Britain and Canada, which led to the conclusion that it was technically possible to build ships from ice, but that during wartime it would be too costly in terms of manpower and strategic materials.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.041
GPT teacher head0.397
Teacher spread0.356 · 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
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

Citations20
Published2004
Admission routes1
Has abstractyes

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