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Record W4295917298 · doi:10.48009/1_iis_2022_115

THE EFFECTS OF 5G IMPLEMENTATION ON THE ASPECTS OF SUSTAINABILITY IN THE AIR TRANSPORTATION INDUSTRY

2022· article· en· W4295917298 on OpenAlexaboutno aff
Leila Halawi, Douglas Ashlock, Lucy Elias, Hannah Puchkoris, Anthony Williams

Bibliographic record

VenueIssues in Information Systems · 2022
Typearticle
Languageen
FieldEngineering
TopicTransport and Logistics Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityBusinessTransport engineeringEngineering

Abstract

fetched live from OpenAlex

The concern of 5G C-band frequency interference with global aviation operations was founded on the criticality of a single-point failure with the radio altimeter operations. The research in this report intends to highlight the severe impact of 5G C-band implementation on the air transportation industry. It focuses on one airport in the United States, Seattle-Tacoma International Airport (SEA), one of the busiest airports in the country, flying both domestically and internationally. The results show that Boeing aircraft are affected more than Airbus for 5G interference. Further, the findings show that the United States should align its 5G implementation with other countries, such as Australia, Canada, the Czech Republic,

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.265
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations3
Published2022
Admission routes1
Has abstractyes

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