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Record W4206307310 · doi:10.31590/ejosat.1039725

Otonom Araçların Benimsenmesi ve Güvenlik Algılarının İncelenmesi

2022· article· tr· W4206307310 on OpenAlexaff
Gözde Bakioğlu, Ali Osman Atahan

Bibliographic record

VenueEuropean Journal of Science and Technology · 2022
Typearticle
Languagetr
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

Çevresel algılama özelliklerine sahip olan seviye “3” otonom araçların çok yakın bir zamanda tüm araçlarda görülmesi beklenirken, sürücünün sürüş görevini ortadan kaldıran seviye "5" otonom araçların ise on sene içerisinde ticarileştirileceği düşünülmektedir. Seviye "5" otonom araçların sürdürülebilir kentsel hareketliliği arttıracağı ve insan kaynaklı hataları azaltıp trafik kazalarını azaltması beklenmektedir. Bununla birlikte bu teknolojinin çeşitli güvenlik sorunlarını da beraberinde getireceği düşünülmektedir. Bu noktada yeni teknoloji ürünü olacak bu araçların benimsenip benimsenmeyeceği önemli bir konudur. Bu çalışmada otonom araçların benimsenmesini etkileyecek faktörler incelenecek olup, aynı zamanda kişilerin bu araçlara karşı güvenlik algıları araştırılacaktır. Araştırma sonucu çıkacak bulguların, otonom araçlar ile ilgili karar vericiler ve politika belirleyiciler için yararlı olması beklenmektedir.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0200.004

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 head0.007
GPT teacher head0.187
Teacher spread0.180 · 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 designObservational
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

Citations1
Published2022
Admission routes1
Has abstractyes

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