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Record W2124206147 · doi:10.1109/secon.2005.1423242

Trajectory Visualization by Using Global Positioning Systems (GPS)

2005· article· en· W2124206147 on OpenAlexaboutno aff
DeWayne Brown, Derrek B. Dunn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsnot available
Fundersnot available
KeywordsGlobal Positioning SystemTrajectorySoftwareComputer scienceVisualizationAssisted GPSAeronauticsReal-time computingSimulationEngineeringOperating systemArtificial intelligence

Abstract

fetched live from OpenAlex

The purpose of this research is to use GPS to visualize the trajectory of space, air, sea, and land vehicles. This research reveals the visualized trajectories of two scenarios. One of the scenarios is an airplane trajectory and the other is a Shuttle launch trajectory. In the first scenario, an airplane flew from Cape Canaveral to Vancouver, Canada. In the second scenario, a rocket was launched from Cape Canaveral and landed near Corpus Christi, Texas. In order to set up these scenarios, we used a GPS simulator, GPS receiver, lap-top computer, MATLAB software, FUGAWI software and Satellite Tool Kit (STK) software. This research can be used in a classroom/laboratory environment to give students hands on experience in the integration of hardware and software equipment used for GPS applications.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.008
GPT teacher head0.242
Teacher spread0.234 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2005
Admission routes1
Has abstractyes

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