MétaCan
Menu
Back to cohort
Record W1691997109 · doi:10.1109/oceans.2001.968233

Power and oxygen sources for a diver propulsion vehicle

2002· article· en· W1691997109 on OpenAlexaff
Graham T. Reader, I.J. Potter, Eric Clavelle

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsUniversity of CalgaryUniversity of Windsor
Fundersnot available
KeywordsPropulsionStirling engineAutomotive engineeringElectric power systemBreadboardPower (physics)Liquid oxygenEngineeringComputer scienceElectrical engineeringMechanical engineeringAerospace engineeringOxygen

Abstract

fetched live from OpenAlex

Diver propulsion vehicles (DPVs) are used by recreational and military divers. For the latter divers there is a need for special DPVs which have low magnetic and acoustic signatures so that they can operate in areas were mines are known or suspected to exist or dumped munitions. The possibility of constructing a power system for such a DPV was investigated. Since commercial off-the-shelf (COTS) products are being used increasingly in military systems a survey and evaluation of existing power systems technologies was conducted. One of the most viable options identified was the use of a Stirling engine. As part of the military specification was the use of a liquid hydrocarbon fuel it was necessary to explore ways in which the necessary oxidant could be provided, using a commercial product, if possible. In the latter case the use of oxygen candles was identified as a viable option. Thus, a Stirling engine system was designed and a breadboard unit constructed and experimental studies of a COTS oxygen candle were conducted. It was determined that such a combination of oxygen source and energy convertor was worthy of further investigation.

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)0.000
metaresearch head score (Gemma)0.000
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: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.010
GPT teacher head0.182
Teacher spread0.172 · 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
GenreOther

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

Citations6
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicSpacecraft and Cryogenic TechnologiesFrench-language works237,207