{"id":"W4295308835","doi":"10.1109/dcoss54816.2022.00055","title":"Keeping Information Alive: Hovering Information and Floating Content Paradigms for Vehicular Networks","year":2022,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Broadcasting (networking); Probabilistic logic; Flooding (psychology); State (computer science); State information; Dissemination; Information resource; Computer network; Telecommunications; Artificial intelligence; Knowledge management","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":[0.00123731,0.0005848277,0.0007343423,0.001192216,0.001238283,0.002572251,0.001915788,0.001680671,0.001278685],"category_scores_gemma":[0.004352161,0.00030214,0.0007109234,0.001332304,0.003353583,0.007685633,0.002677501,0.002418012,0.0004969015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001230882,"about_ca_system_score_gemma":0.0007563236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002710403,"about_ca_topic_score_gemma":0.002482051,"domain_scores_codex":[0.9989384,0.0003292636,0.00006518696,0.0001895144,0.0003576648,0.0001200504],"domain_scores_gemma":[0.9976884,0.001130276,0.0002935404,0.0003414743,0.0003708994,0.0001753552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001203197,0.00005137277,0.0008607099,0.0003135858,0.00004454193,0.0003925786,0.001181753,0.04453951,0.00413852,0.8355422,0.00494756,0.1078673],"study_design_scores_gemma":[0.000024294,0.0002173701,0.0005864931,0.0001936631,0.00006991714,0.000952583,0.001105504,0.2490991,0.002832256,0.6717253,0.07309881,0.00009472535],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01650637,0.01049484,0.9409988,0.004915094,0.0008421385,0.0001289987,0.0001060767,0.000180178,0.02582753],"genre_scores_gemma":[0.7673979,0.01671972,0.196248,0.001258988,0.001983114,0.0003850739,0.0002490192,0.0001215512,0.0156367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002710403,"threshold_uncertainty_score":0.008930683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009553280651843712,"score_gpt":0.1787691021222748,"score_spread":0.1692158214704311,"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."}}