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Record W4404727913 · doi:10.1016/j.trd.2024.104522

Global Airport Resilience Index: Towards a comprehensive understanding of air transportation resilience

2024· article· en· W4404727913 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

VenueTransportation Research Part D Transport and Environment · 2024
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsResilience (materials science)Index (typography)Transport engineeringEnvironmental planningBusinessEngineeringEnvironmental scienceEnvironmental resource managementComputer science

Abstract

fetched live from OpenAlex

Estimating the vulnerability of airport outages on the air transportation system is an ongoing research challenge. While existing studies are predominantly focused on the analysis of the air-side airport network, with airports being nodes and links representing direct flights, in this study we propose the so-called Global Airport Resilience Index for worldwide airports which incorporates ground infrastructure as well as population distribution for the computation of an integrated resilience index that estimates the effects of airport disruptions on the entire system. Based on the Global Airport Resilience Index of airports, we can derive realistic assessment for airport resilience worldwide, where a more important airport has a higher index value. The inherent challenges in data management and computation are significant and require sophisticated solutions. Overall, we believe that our study provides novel insights into air transportation and airport resilience, by consideration of a more realistic resilience estimation measure.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
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
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.037
GPT teacher head0.295
Teacher spread0.259 · 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