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Record W2734684871 · doi:10.1109/eucnc.2017.7980733

Traffic safety in the METIS-II 5G connected cars use case: Technology enablers and baseline evaluation

2017· article· en· W2734684871 on OpenAlexaboutno aff
David Martín‐Sacristán, Carlos Herranz, José F. Monserrat

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsnot available
Fundersnot available
KeywordsBaseline (sea)MetisStandardizationComputer scienceUnanimityBandwidth (computing)Systems engineeringTelecommunicationsEngineeringOperating systemDatabase

Abstract

fetched live from OpenAlex

Ultra-reliable V2V communications with extreme transmission rate probably constitute the most ambitious use case of the fifth generation mobile. At present, both the scientific community and the standardization bodies are addressing the design of the technologies that will make it possible, although there is no unanimity in which technologies to incorporate. This paper approaches this topic from the work developed in the METIS-II project, describing the use case, the technology enablers and some details of the evaluation of these techniques in realistic scenarios. Results show that, for a baseline system, carrier bandwidths needed to fulfil the requirements are between 30 and 100 MHz, depending on the scenario. Nevertheless, results show potential to reduce the needed bandwidth to a range between 20 and 50 MHz by incorporating additional technology enablers to the studied baseline system.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.283
Teacher spread0.244 · 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 designBench or experimental
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

Citations13
Published2017
Admission routes1
Has abstractyes

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