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Record W2897421503 · doi:10.1155/2018/3984962

A Ship Domain-Based Method of Determining Action Distances for Evasive Manoeuvres in Stand-On Situations

2018· article· en· W2897421503 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Advanced Transportation · 2018
Typearticle
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsCollision avoidanceDomain (mathematical analysis)Reduction (mathematics)CollisionStability (learning theory)Moment (physics)Action (physics)ContinuationComputer scienceMarine engineeringCover (algebra)EngineeringSimulationMathematicsComputer security

Abstract

fetched live from OpenAlex

A ship encounter can be considered safe if neither of ships’ domains (defined areas around ships) is intruded by other ships. Published research on this includes optimising collision avoidance manoeuvres fulfilling domain-based safety conditions. However, until recently there was no method, using ship’s domain to determine exact moment when a particular collision avoidance manoeuvre can still be successfully performed. The authors have already proposed such method for give-way encounters. In the paper, documenting continuation of the research, another kind of scenarios is considered. This paper is focused on situations where the own ship is the stand-on one and the target is supposed to manoeuvre. The presented method uses a ship’s dynamics model to compute distance necessary for a manoeuvre successful in terms of avoiding domain violations. Additionally, stability-related phenomena and their impact on possible manoeuvres in heavy weather are taken into account. The method and applied models are illustrated in a series of simulation results. The simulations cover various examples of stand-on situations, including encounters in heavy weather conditions. Discussed manoeuvres may be limited to course alteration or may combine turns with speed reduction.

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.

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: none
Teacher disagreement score0.787
Threshold uncertainty score0.309

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 head0.023
GPT teacher head0.326
Teacher spread0.304 · 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