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Record W1923135191 · doi:10.1109/uust.1987.1158594

Evolution of the dolphin multi-vehicle control system

2005· article· en· W1923135191 on OpenAlexaffabout
B. Butler, S. Maryka

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsCoquitlam College
Fundersnot available
KeywordsControl systemComputer scienceMarine engineeringHydrographyEngineeringReal-time computingGeographyElectrical engineering

Abstract

fetched live from OpenAlex

This paper describes the evolution of the DOLPHIN multivehicle computer control system. The unmanned, untethered DOLPHIN semi-submersible was designed as a stable platform for hydrographic research. A proof-of-concept DOLPHIN system was developed in 1983 for the Canadian Hydrographic Service (CHS), consisting of one vehicle controlled by a single operator console over a real time radio link. In 1985, the DOLPHIN system was expanded to control three vehicles by time multiplexing the radio link. The console was expanded to allow simultaneous control of all three vehicles. Presently, the DOLPHIN system is being expanded to eight vehicles, with a network of three consoles. Control of a vehicle can be passed from one console to another, allowing flexible mission configurations. In addition, the DOLPHIN vehicles will have increased autonomy, such as automated launch/recovery, positioning and course following, and more fault tolerant software. When complete, the system will provide a cost-effective means of conducting multi-disciplinary surveys offshore.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.190
Teacher spread0.180 · 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 designBench or experimental
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 routes2
Has abstractyes

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