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Record W4309855151 · doi:10.3390/su142315630

Maritime Autonomous Surface Ships: Problems and Challenges Facing the Regulatory Process

2022· article· en· W4309855151 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsCegep de Sept IlesUniversité du Québec à Rimouski
Fundersnot available
KeywordsSoftware deploymentProcess (computing)Maritime industryPosition (finance)Sustainable developmentRisk analysis (engineering)BusinessElement (criminal law)Process managementComputer sciencePolitical scienceCommerceLaw

Abstract

fetched live from OpenAlex

Technological innovation constantly transforms and redefines the human element’s position inside complex socio-technical systems. Autonomous operations are in various phases of development and practical deployment across several transport domains, with marine operations still in their infancy. This article discusses current trends in developing autonomous vessels and some of the most recent initiatives worldwide. It also investigates the individual and combined effects of maritime autonomous surface ships (MASS) on regulations, technology, and sectors in reaction to the new marine paradigm change. Other essential topics, such as safety, security, jobs, training, and legal and ethical difficulties, are also considered to develop a solution for efficient, dependable, safe, and sustainable shipping in the near future. Finally, it is advised that holistic approaches to building the technology and regulatory framework be used and that communication and cooperation among various stakeholders based on mutual understanding are essential for the MASS to arrive in the maritime industry successfully.

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)0.001
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 score0.502
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.220
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