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Record W2586483973 · doi:10.1016/j.procs.2017.01.141

Causal Analysis of Airline Trajectory Preferences to Improve Airspace Capacity

2017· article· en· W2586483973 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

VenueProcedia Computer Science · 2017
Typearticle
Languageen
FieldComputer Science
TopicConstraint Satisfaction and Optimization
Canadian institutionsYork University
FundersEuropean Commission
KeywordsComputer scienceAir traffic controlWorkloadTrajectoryNational Airspace SystemInterdependenceSeparation (statistics)Constraint (computer-aided design)Constraint programmingRange (aeronautics)Operations researchAir traffic managementTrajectory optimizationFree flightMathematical optimizationSimulationAerospace engineering

Abstract

fetched live from OpenAlex

The problem of fitting the maximum number of aircraft into ATC sectors, keeping in mind aircraft separation and safety standards, area navigation direct routings and other factors, is known as the airspace capacity problem. Above the European airspace, a high density network of air traffic can be found which is determined by the workload of controllers. Constraint Programming (CP) is a powerful powerful paradigm for representing and solving a wide range of combinatorial problems. The PARTAKE project fosters adherence of air space user's trajectory preferences enhancing Trajectory Based Operations (TBO) concepts by identifying tight interdependencies between trajectories and introducing a new mechanism to improve aircraft separation at the hot spots by the mean of CP. The underlying philosophy is to capitalize present freedom degrees between layered ATM planning tools, when sequencing departures at airports by considering the benefits of small time stamp changes in the assigned slot departures.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.750
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.024
GPT teacher head0.268
Teacher spread0.245 · 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