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Record W4393505652 · doi:10.5281/zenodo.7074324

Classification of Artificial Intelligence and eXplainable Artificial Intelligence publications in Air Traffic Management

2022· dataset· en· W4393505652 on OpenAlexaff
Augustin Degas, Mir Riyanul Islam, Christophe Hurter, Shaibal Barua, Hamidur Rahman, Minesh Poudel, Daniele Ruscio, Mobyen Uddin Ahmed, Shahina Begum, Md Aquif Rahman, Stefano Bonelli, Giulia Cartocci, Gianluca Di Flumeri, Gianluca Borghini, Pietro Aricò, Fabio Babiloni

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldEngineering
TopicAir Traffic Management and Optimization
Canadian institutionsDeep River Science Academy
FundersHorizon 2020 Framework Programme
KeywordsArtificial intelligenceComputer scienceData science

Abstract

fetched live from OpenAlex

v1.0 version used and partially published in "A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory". In this version, it references mainly Transportation Reasearch Part C, ICRAT, Journal of ATM, and ATM Seminar, IEEE transaction on ITS, but not only

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.032
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0090.011
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0320.062

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 head0.053
GPT teacher head0.250
Teacher spread0.197 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)Same topicAir Traffic Management and OptimizationFrench-language works237,207