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Record W1556276806

Facilitating cooperative AUV missions: experimental results with an acoustic knowledge-sharing framework

2013· article· en· W1556276806 on OpenAlexaff
Zeyn Saigol, Gordon Frost, Nikolaos Tsiogkas, Francesco Maurelli, David M. Lane, Alex Bourque, Bao Nguyen

Bibliographic record

VenueOCEANS Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsComputer scienceOntologyBandwidth (computing)RobotService (business)Transmission (telecommunications)Network packetInformation sharingDomain (mathematical analysis)Underwater acoustic communicationUnderwaterReal-time computingTelecommunicationsComputer networkDistributed computingWorld Wide WebArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

We describe and experimentally evaluate a decentralised world model for sharing data over limited bandwidth and high loss channels, such as encountered in the underwater domain. This world model service enables information extracted from the environment to be stored and queried using an ontology format. Besides providing an information storage facility, the world model manages all acoustic communications, and ensures that the shared ontology is updated on all robots while minimising transmissions. Using this world model service, a collaborative mission scenario of mine counter-measures is described, where the world model aids in the efficient use of the broadcast medium. Inwater experiments conducted in Loch Earn, Scotland, confirmed that the world model functioned correctly with a team of two AUVs. Early results for the efficiency of the system are also presented, which show that the world model can continue to function at relatively high packet error rates, although the error rate increased rapidly with transmission distance in our test environment.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.629

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 head0.042
GPT teacher head0.277
Teacher spread0.235 · 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 designSimulation or modeling
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

Citations10
Published2013
Admission routes1
Has abstractyes

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