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Record W2979558944 · doi:10.1109/csci46756.2018.00034

Multi-Criteria Weather Routing Optimization Based on Ship Navigation Resistance, Risk and Travel Time

2018· article· en· W2979558944 on OpenAlex
Tommaso Fabbri, Raúl Vicen-Bueno, Aren Hunter

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMaritime Navigation and Safety
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsRouting (electronic design automation)Computer scienceResistance (ecology)Travel timeWeather forecastingOperations researchReal-time computingMeteorologyTransport engineeringEngineeringGeographyComputer network

Abstract

fetched live from OpenAlex

This paper presents the analysis of the weather routing scenario in a multi-criteria setup. The set of 3 conflicting criteria is: added navigation resistance (caused by wind and waves), navigation risk and travel time. To this aim the International Maritime Organization (IMO) safety guidelines are exploited for the design of navigation risk criterion as function of the METeorological and OCeanographic (METOC) and sailing conditions. This is directly integrated in the multi-criteria setup. The proposed methodology is tested in an operational scenario in the Mediterranean Sea showing the different alternatives to the decision-makers.

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 categoriesInsufficient payload (model declined to judge)
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 score0.912
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)0.0010.000

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 head0.009
GPT teacher head0.228
Teacher spread0.218 · 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

Quick stats

Citations9
Published2018
Admission routes1
Has abstractyes

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Same topicMaritime Navigation and SafetyFrench-language works237,207