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Record W4405077066 · doi:10.1504/ijstl.2024.143138

Disruption risks to global container shipping network in the presence of COVID-19 pandemic: a static structure and dynamic propagation perspective

2024· article· en· W4405077066 on OpenAlexaboutno aff
Xiongping Yue, Xiaochun Chen, Shuai Liu, Huanyu Ren

Bibliographic record

VenueInternational Journal of Shipping and Transport Logistics · 2024
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Container (type theory)Coronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Computer scienceBusinessRisk analysis (engineering)VirologyMedicineOutbreakEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This study examines the disruption risks of the global container shipping network from a static structure and dynamic propagation perspective. First, the topological structure of the global container shipping network is investigated by constructing a weighted and directed network. Second, the hidden risks are uncovered by the proposed disruption risk propagation model in the global container shipping network. The results indicate that the global container shipping network is characterised by 'hub and spoke' feature with a 'robust yet fragile' structure. Ports in Asia play an essential role in the global container shipping network. Additionally, risky outbound ports are located in China, and risky inbound ports are located in India and Canada. The hidden shipping route risks are focused on China and Singapore, South Korea. Finally, the propagation speed of port inbound disruption is faster than that of port outbound disruption.

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 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 score0.626
Threshold uncertainty score0.313

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 head0.038
GPT teacher head0.337
Teacher spread0.299 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2024
Admission routes1
Has abstractyes

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