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Record W1919625923 · doi:10.1109/oceans.2001.968764

C-SCOUT maneuverability-a study in sensitivity

2002· article· en· W1919625923 on OpenAlexafffundabout
D. Perrault, T Curtis, Neil Bose, Siu O’Young, Christopher Williams

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsMemorial University of Newfoundland
FundersMemorial University of NewfoundlandVictoria UniversityUniversity of Victoria
KeywordsTestbedPropulsionModular designMarine engineeringUnderwaterSensitivity (control systems)Key (lock)Systems engineeringEngineeringVehicle dynamicsRemotely operated underwater vehicleComputer scienceAerospace engineeringMobile robotRobotArtificial intelligence

Abstract

fetched live from OpenAlex

In September of 1998, the Institute for Marine Dynamics (IMD) of the National Research Council of Canada and the Ocean Engineering Research Centre of Memorial University of Newfoundland commenced a collaborative effort to design a streamlined autonomous underwater vehicle (AUV). This AUV, the Canadian Self-Contained Off-the-shelf Underwater Testbed (C-SCOUT), is intended to serve as a test bed for systems research; in particular it is expected to assist in the development of control systems and propulsion systems. It is also to be utilized in the testing of vehicle components and as a general research and development tool for years to come. The vehicle is of modular construction, such that its length, and the position of the appendages are somewhat variable. One of the key elements in the vehicle's effectiveness as a test bed is a fundamental understanding of its maneuverability and of its sensitivity to changes in hydrodynamic parameters. Hydrodynamic parameters, however, are typically determined from in-water testing and there is usually some uncertainty concerning their exact values. Knowledge of these vehicle characteristics will allow the systems designer to separate inherent vehicle behaviour from behaviour induced by the system being tested, resulting in a clear measurement of system performance.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.227

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 head0.034
GPT teacher head0.217
Teacher spread0.183 · 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 designObservational
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
Published2002
Admission routes3
Has abstractyes

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