{"id":"W4308990634","doi":"10.1109/tvt.2022.3214389","title":"IEEE Vehicular Technology Society Information","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Digital Media and Visual Art","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Engineering; Telecommunications; Systems 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":[0.0002286766,0.000271054,0.000290849,0.001031347,0.0007463574,0.00009253815,0.00147273,0.0003374634,0.00005134182],"category_scores_gemma":[0.00001000193,0.0002863555,0.0002487447,0.002912505,0.0002336881,0.0009012353,0.00003895607,0.00108067,0.0003479012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002176312,"about_ca_system_score_gemma":0.0001216653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007161448,"about_ca_topic_score_gemma":0.000002652838,"domain_scores_codex":[0.9979816,0.00004779765,0.0004130704,0.0004833347,0.0005240849,0.0005501304],"domain_scores_gemma":[0.998612,0.00004222244,0.0001368383,0.0009968482,0.0001242142,0.00008787938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005276623,0.001812187,0.0001160592,0.00008578529,0.0004922269,0.0001845834,0.001393715,0.0659151,0.02848547,0.08750102,0.005247439,0.8087136],"study_design_scores_gemma":[0.002443979,0.002814141,0.00003327677,0.00004482611,0.00008694531,0.00102511,0.001634157,0.168556,0.3823355,0.02492606,0.4146849,0.001415218],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1159772,0.0000682819,0.8716593,0.008024419,0.001464837,0.0004021394,0.00002323592,0.002022924,0.0003576103],"genre_scores_gemma":[0.9918759,0.00003083549,0.006149042,0.001147055,0.00001804295,0.000572017,0.000005906372,0.00001971135,0.000181525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8758986,"threshold_uncertainty_score":0.9999589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008872243604830143,"score_gpt":0.2274611068399111,"score_spread":0.2185888632350809,"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."}}