{"id":"W3143314798","doi":"10.1109/mcomstd.2021.9392785","title":"Guest Editorial: Data Analytics Streamlines Autonomous Driving","year":2021,"lang":"en","type":"editorial","venue":"IEEE Communications Standards Magazine","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Exfo Electro-Optical Engineering (Canada)","funders":"","keywords":"Big data; Computer science; Automation; Augmented reality; Set (abstract data type); Analytics; Human–computer interaction; Key (lock); Virtual reality; Data science; Artificial intelligence; Data mining; Computer security; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001141215,0.0005413151,0.0006946197,0.0003519599,0.0002691241,0.0003987798,0.004547459,0.0008116598,0.00002989118],"category_scores_gemma":[0.00111192,0.0006284403,0.000134616,0.0005454851,0.000194882,0.0004065534,0.001655908,0.001701761,0.00003771054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006904523,"about_ca_system_score_gemma":0.000692161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002622062,"about_ca_topic_score_gemma":0.001354434,"domain_scores_codex":[0.9966812,0.0001362635,0.0008544023,0.0005738122,0.001343186,0.0004111931],"domain_scores_gemma":[0.9889933,0.0006004542,0.0002023406,0.008860183,0.001189379,0.0001542987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003587452,0.00007842613,0.000001971089,0.0001527876,0.0002819219,0.000005978336,0.00002895374,0.0005137838,0.00003311461,0.00004167533,0.9955831,0.00327474],"study_design_scores_gemma":[0.0003468953,0.00002838555,0.00000235097,0.0003221029,0.000315463,0.000001153386,0.00002196568,0.02954175,0.00002288896,0.00001697604,0.9688582,0.0005218649],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[6.777875e-7,0.002559372,0.03373706,0.0002009079,0.9325992,0.0003313274,0.01077849,0.00733738,0.01245558],"genre_scores_gemma":[0.0007813768,0.02784973,0.005303467,0.00001084878,0.9461164,0.00008549203,0.01885152,0.0001887812,0.0008123408],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.02902797,"threshold_uncertainty_score":0.9996167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02961020465889903,"score_gpt":0.3144424575640681,"score_spread":0.2848322529051691,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}