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On the contagion leakage via incoming flights during China’s aviation policies in the fight against COVID-19

2023· article· en· W4319442057 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

VenueJournal of Air Transport Management · 2023
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsAviationChinaContext (archaeology)Coronavirus disease 2019 (COVID-19)PandemicCommercial aviationAir travelAviation safetyMechanism (biology)Air traffic controlComputer scienceOperations researchAeronauticsGeographyEngineeringMedicineAerospace engineeringCartographyPhysics

Abstract

fetched live from OpenAlex

For nearly three years with the COVID-19 pandemic, China has implemented a set of strict policies to control the flux of potential virus carriers in cross-border flights: The so-called Circuit Breaker mechanism. In this study, we review the evolution of this mechanism - a rather unique experiment in the global aviation system - from a data-driven perspective. Specifically, we perform an investigation on the extent of violations and their potential drivers. In total, 183 events are analyzed covering the period from epidemic outbreak in early 2020 to December 2021. In addition to describing the spatial extent and temporal evolution, we develop a regression model which helps us to better understand the universal patterns. By dissecting an under-investigated phenomenon, we believe that our study contributes to the rich literature on aviation and COVID-19, not only in the specific context of China, but also by assessing some of the challenges and potential of containing a global health threat using strict aviation policies.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
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 head0.115
GPT teacher head0.372
Teacher spread0.258 · 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