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Dynamic Path Following in Ice-covered Waters with an Autonomous Surface Ship Model

2021· article· en· W4212897324 on OpenAlexaff
Kevin Murrant, Robert Gash, Jason Mills

Bibliographic record

VenueOCEANS 2021: San Diego – Porto · 2021
Typearticle
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAutopilotTestbedHeading (navigation)Marine engineeringScope (computer science)Computer sciencePath (computing)Sea iceSimulationEnvironmental scienceAerospace engineeringSystems engineeringEngineeringGeologyOceanography

Abstract

fetched live from OpenAlex

This paper describes the development and testing of an autonomous surface ship model in a marine test basin containing model scaled ice. Scope of this paper includes a description of the simulation environment used to develop the autopilot capability, ship modeling approach, path following methodology, physical model configuration and setup, and test basin environment. Tests demonstrated the successful integration of the testbed platform to conduct free-running model experiments in navigation & control. Results indicate that conventional methods for path following and heading control are not sufficient for navigation in ice-covered waters and further research is warranted.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

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 head0.008
GPT teacher head0.213
Teacher spread0.206 · 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.

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

Citations3
Published2021
Admission routes1
Has abstractyes

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Same venueOCEANS 2021: San Diego – PortoSame topicMaritime Navigation and SafetyFrench-language works237,207