{"id":"W3099551741","doi":"10.1002/ett.4168","title":"Mining large‐scale high utility patterns in vehicular ad hoc network environments","year":2020,"lang":"en","type":"article","venue":"Transactions on Emerging Telecommunications Technologies","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Vehicular ad hoc network; Wireless ad hoc network; Pruning; Mobile ad hoc network; Set (abstract data type); Big data; Distributed computing; Data mining; Computer network; Wireless","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":[],"consensus_categories":[],"category_scores_codex":[0.0002549043,0.0002189263,0.0002400526,0.0001806669,0.0006020588,0.00009027364,0.002647649,0.0001499623,0.00003618348],"category_scores_gemma":[0.00003047088,0.0002384387,0.00008179928,0.001327421,0.0001058556,0.0003996697,0.0001980635,0.0006377429,0.00006400398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006854763,"about_ca_system_score_gemma":0.00002851042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002193733,"about_ca_topic_score_gemma":0.0001193253,"domain_scores_codex":[0.9982698,0.00008904391,0.0004218532,0.0005660888,0.0002027082,0.0004505383],"domain_scores_gemma":[0.9974058,0.0001469795,0.0001187744,0.002246836,0.00001989958,0.00006174177],"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":[0.00001029155,0.0008818396,0.001713232,0.00002223064,0.00006978917,0.000005590536,0.003010058,0.01609859,0.0003075689,0.003126721,0.0006844034,0.9740697],"study_design_scores_gemma":[0.001305365,0.0002980935,0.0152635,0.0001851597,0.00005558483,0.000009265045,0.007908607,0.817211,0.004852114,0.003077948,0.1487232,0.001110199],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04045342,0.0004879825,0.9330459,0.02414031,0.00005551998,0.0002832808,0.00005362599,0.001376745,0.0001032403],"genre_scores_gemma":[0.7028777,0.001438905,0.2951406,0.0002074074,0.000005871097,0.0002579288,0.00003290889,0.00001509188,0.00002359279],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9729595,"threshold_uncertainty_score":0.9723247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02106311454404438,"score_gpt":0.2487440567049216,"score_spread":0.2276809421608772,"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."}}