{"id":"W2017182448","doi":"10.1145/2815347.2830325","title":"HCI in VANET IR-CAS","year":2015,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Context (archaeology); Usability; Interface (matter); Eye tracking; Relevance (law); Selection (genetic algorithm); Crash; User interface; Human–computer interaction; Visualization; Computer vision; Artificial intelligence; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001696141,0.0009035876,0.0003918817,0.001018434,0.0004028151,0.003016978,0.001564889,0.001024764,0.02106078],"category_scores_gemma":[0.004120973,0.000418283,0.0005039196,0.0005702084,0.0007509144,0.001952227,0.002157713,0.0007806363,0.004539429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006860103,"about_ca_system_score_gemma":0.0006648831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003909932,"about_ca_topic_score_gemma":0.004489521,"domain_scores_codex":[0.9984254,0.0005298658,0.00009026215,0.0002862554,0.0004821024,0.0001861458],"domain_scores_gemma":[0.998548,0.0005383854,0.00004774309,0.0002359647,0.000472103,0.0001578263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001939374,0.0004250219,0.007280048,0.002049555,0.0001292814,0.001259708,0.003920804,0.005784993,0.1115204,0.03539627,0.04629172,0.7840028],"study_design_scores_gemma":[0.0003842321,0.003129662,0.02193067,0.001206848,0.0003627888,0.007456476,0.004495255,0.1069006,0.05033008,0.02407658,0.7791186,0.0006082778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1409129,0.005525386,0.6309594,0.001942113,0.001512077,0.001306169,0.001174315,0.02745245,0.1892152],"genre_scores_gemma":[0.6569805,0.001432124,0.2443817,0.001271242,0.0002247169,0.00090223,0.001498469,0.001244961,0.09206414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02106078,"threshold_uncertainty_score":0.07045537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08210273600636658,"score_gpt":0.2845980853635263,"score_spread":0.2024953493571597,"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."}}