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Record W2961832654 · doi:10.1016/j.ress.2019.106584

A systemic hazard analysis and management process for the concept design phase of an autonomous vessel

2019· article· en· W2961832654 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueReliability Engineering & System Safety · 2019
Typearticle
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsDalhousie University
FundersEuropean Regional Development FundOcean Frontier InstituteLiikenteen Turvallisuusvirasto TrafiTekes
KeywordsProcess (computing)Context (archaeology)HazardHazard analysisPhase (matter)Risk analysis (engineering)EngineeringSystems engineeringComputer scienceBusinessReliability engineering

Abstract

fetched live from OpenAlex

<p>Autonomous vessels have become a topic of high interest for the maritime transport industry. Recent progress in the development of technologies enabling autonomous systems has fostered the idea that autonomous vessels will soon be a reality. However, before the first autonomous vessel can be released into her actual context of operation, it is necessary to ensure that it is safe. This is a major challenge as the experience of autonomous ships is very limited. This study highlights the need for elaborating a systemic and systematic hazard analysis since the earliest design phase of an autonomous vessel. In particular, it proposes a process for elaborating an initial hazard analysis and management that provides coherent, transparent and traceable safety input information for the design of an autonomous vessel. The process is applied to analyse the hazards of two autonomous vessel concepts for urban transport in the city of Turku, Finland.</p>

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.239
Teacher spread0.230 · 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