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Record W2118096804 · doi:10.1109/uust.1989.754707

Non-Nuclear Powerplants for Auvs

2005· article· en· W2118096804 on OpenAlexaff
Graham T. Reader, G. Walker, J G Hawley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsUnderwaterNuclear powerNuclear power plantProcess (computing)Marine engineeringEngineeringSelection (genetic algorithm)Environmental scienceComputer scienceSystems engineeringGeology

Abstract

fetched live from OpenAlex

There is a growing need for underwater vehicles both military and commercial to stay underwater longer without the requirement for surface support. Almost unlimited endurance can be obtained by having a nuclear power source. However, present nuclear powered vessels have displacements of at least 3000 tonnes, and although it is possible that small nuclear vessels could be developed, the costs involved may be prohibitive. Thus alternatives to the present lead acid battery systems have been sought. This paper examines the likely alternatives and discusses methods which may be used to aid the selection process between these alternatives. It is concluded that the three main candidates, the Closed-cycle Diesel, the Stirling and the Fuel Cell, are viable systems and that all three could find potential uses in the underwater environment. Deficiencies in current powerplant selection techniques are discussed.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.302

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)0.0000.000

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 head0.006
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 teacher head, 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

Citations3
Published2005
Admission routes1
Has abstractyes

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