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Record W4383560516 · doi:10.54254/2755-2721/5/20230674

Analysis of China’s aviation network and the key node

2023· article· en· W4383560516 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

VenueApplied and Computational Engineering · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicComplex Network Analysis Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCentralityAviationNode (physics)Key (lock)Degree distributionNetwork analysisCommercial aviationComputer scienceNetwork scienceComplex networkAviation engineeringTransport engineeringEngineeringComputer securityCivil aviationMathematicsAerospace engineering

Abstract

fetched live from OpenAlex

Aviation network plays a crucial role in the economic development.However, over these years, the epidemic of COVID-19 seriously influenced the aviation network, including airport shutdowns and a large number of flight cancellations, which causes serious economic losses. Our team examined the topology traits of China’s aviation network in our research. The details of the aviation network, including airports as nodes and air routes as edges, are visualised in the study. Essential factors of the network are examined, including degree distribution, clustering coefficient, centrality, etc. Gephi is utilized to visualise the communities of the aviation network. The study analyses these factors and provides a case analysis of how the aviation network will behave if one important node is removed. In the study, it is also discussed the centrality, how the communities change and whether the network collapses to gain a clear understanding of the airports as nodes in the network.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.645
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.202
Teacher spread0.198 · 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