{"id":"W3024137225","doi":"10.1109/tvt.2020.2994181","title":"Smart Proactive Caching: Empower the Video Delivery for Autonomous Vehicles in ICN-Based Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cache; Quality of experience; Computer network; Augmented reality; Popularity; Multimedia; Quality of service; Human–computer interaction","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005366026,0.0006444243,0.0007159993,0.0004926353,0.0007296845,0.0008906039,0.001402444,0.0006560651,0.0005747725],"category_scores_gemma":[0.001537268,0.0002199697,0.0002935791,0.0007285616,0.0003882592,0.001671329,0.0006125426,0.0004931075,0.0002094073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001139233,"about_ca_system_score_gemma":0.001320521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02113636,"about_ca_topic_score_gemma":0.02803195,"domain_scores_codex":[0.9996414,0.00007003743,0.0000190774,0.00008233763,0.00008007638,0.0001070713],"domain_scores_gemma":[0.9993856,0.0001428642,0.00006229617,0.00009734336,0.0002531489,0.00005871421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006737935,0.0003058279,0.008787371,0.0003573878,0.0001650697,0.0009985493,0.0007524381,0.459971,0.06460787,0.03073893,0.0190421,0.4135998],"study_design_scores_gemma":[0.000008204285,0.00006254066,0.0003666268,0.00001145491,0.0000255538,0.0001164998,0.00008516551,0.9903515,0.003113218,0.002852765,0.002989132,0.00001731297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1589206,0.004316818,0.8255417,0.0007693539,0.000379569,0.0002157703,0.0002503767,0.002123436,0.007482475],"genre_scores_gemma":[0.9350071,0.0008457986,0.06128448,0.0002378102,0.00008834124,0.00005327579,0.0002054105,0.00003751031,0.002240288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02113636,"threshold_uncertainty_score":0.0420267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01667388066321698,"score_gpt":0.2186177178035112,"score_spread":0.2019438371402942,"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."}}