MétaCan
Menu
Back to cohort
Record W4232971337 · doi:10.13111/2066-8201.2013.5.3.9

Algorithm for the aircraft trajectories considering No Fly Zones for a Flight Management System

2013· article· en· W4232971337 on OpenAlexaff
Julien Fays

Bibliographic record

VenueINCAS BULLETIN · 2013
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Control Systems
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsComputer scienceAerospace engineeringFlight management systemAeronauticsFly-by-wireReal-time computingEnvironmental scienceSystems engineeringFlight simulatorSimulationEngineering

Abstract

fetched live from OpenAlex

In this paper, optimal four dimensions trajectories (three space dimensions and one time dimension) are designed using new conceived algorithms, to connect all waypoints specified in the flight plan of an aircraft.The designs of these trajectories also include the management of No-Fly-Zones, which are zones over which flying is forbidden or temporarily refused.These zones can be either a meteorological obstacle to by-pass or zones in which air conflicts would be already too dense to allow the flying of a new aircraft.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.005
GPT teacher head0.174
Teacher spread0.169 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations8
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueINCAS BULLETINSame topicAerospace Engineering and Control SystemsFrench-language works237,207