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Record W4245942619 · doi:10.4031/mtsj.43.3.10

ROV (<i>Suijin</i>)

2009· article· en· W4245942619 on OpenAlexaboutno aff
Brody Morrison, Andrew Maillet, M. G. Brown, Leslie Holloway, Jade Moss, Lindsay Holloway, Danielle Howse, Michelle J. White, Suyen Oldford, Jonathan Young, M. Chaffey, Shawn Collins, Michael Coles, Benji Penney, Gavin Diamond

Bibliographic record

VenueMarine Technology Society Journal · 2009
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsnot available
Fundersnot available
KeywordsRemotely operated underwater vehicleSubmarineAeronauticsClass (philosophy)EngineeringMarine engineeringLogbookFrame (networking)Computer scienceArtificial intelligenceRobotTelecommunicationsGeologyOceanographyMobile robot

Abstract

fetched live from OpenAlex

Abstract The &lt;xref ref-type="bibr" rid="bib2"&gt;2009 Marine Advanced Technology Education (MATE)&lt;/xref&gt; remotely operated vehicle (ROV) competition focuses on a submarine rescue training exercise. There are four tasks outlined for the Ranger class, each providing its own challenge. Our ROV was designed to carry out these tasks with precision and agility.The team spent numerous hours planning, building, and field testing our ROV. We had to be prepared to combat technical problems and overcome the challenge of differing opinions. Because of the diversity of the tasks, Suijin had to be very well designed. This required the creation of a rigid frame, useful end effectors, and a versatile propulsion system; a form of buoyancy, effective sensors, and proper wiring were also necessary. There were many ideas to consider and obstacles to overcome, but finally, we completed our masterpiece.Heritage Robotics is very pleased to present the following technical report, which communicates the details of Suijin , an ROV created by students from Heritage Collegiate, Lethbridge, Newfoundland, Canada. This document includes detailed descriptions and diagrams of Suijin ’s components, possible future improvements, trouble shooting techniques, the lessons we learned, the challenges we faced, information on the Submersible LR5, reflections, a thorough budget, and acknowledgments of all those who helped along the way.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.442

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 integrity0.0000.001
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 head0.005
GPT teacher head0.192
Teacher spread0.187 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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