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Record W2353949500

The Arithmetic which is Used by Cab Integrated Radio Equipment to Make Sure the Target Point Based on GPS Data

2005· article· en· W2353949500 on OpenAlexvenueno aff
Limin Jia

Bibliographic record

VenueMicrocomputer applications · 2005
Typearticle
Languageen
FieldEngineering
TopicIoT and GPS-based Vehicle Safety Systems
Canadian institutionsnot available
Fundersnot available
KeywordsGlobal Positioning SystemComputer sciencePoint (geometry)Function (biology)Geographic coordinate systemReal-time computingSoftwareTelecommunicationsGeodesyOperating system
DOInot available

Abstract

fetched live from OpenAlex

This article discusses the algorithm which is used by CIR to make sure the train’s target point based on GPS data. First, we introduce the software construction and main function of CIR and then the application of GPS. This Arithmetic read the GPRMC formatted data through RS-422 serial port of CIR's GPS model, and then abstract the effective data of the latitude and longitude. Based on analysis and computing, we can identify the area where the locomotive is located and finally determine the target point it is going to arrive dynamically according to direction, target point, and tendency of movement of locomotive.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.873
Threshold uncertainty score0.709

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.228
Teacher spread0.218 · 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 designNot applicable
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

Citations0
Published2005
Admission routes1
Has abstractyes

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