{"id":"W3154394899","doi":"10.1155/2021/8835859","title":"Context-Aware Services Using MANETs for Long-Distance Vehicular Systems: A Cognitive Agent-Based Model","year":2021,"lang":"en","type":"article","venue":"Scientific Programming","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"NetLogo; Computer science; Context (archaeology); Traffic congestion; Intelligent transportation system; Scarcity; Fleet management; Operations research; Transport engineering; Computer security; Risk analysis (engineering); Telecommunications; Engineering; Business","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":[0.0004190708,0.0009752115,0.0008355372,0.0006811539,0.001025184,0.002350032,0.002148272,0.001983982,0.002994188],"category_scores_gemma":[0.0009806551,0.000417578,0.001090909,0.0009642853,0.0008667013,0.00199944,0.001403793,0.00137093,0.0006461762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001532945,"about_ca_system_score_gemma":0.001611162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02279613,"about_ca_topic_score_gemma":0.01611645,"domain_scores_codex":[0.9995876,0.0001302805,0.00002274493,0.00009870086,0.0000857905,0.00007486535],"domain_scores_gemma":[0.9996834,0.0001246468,0.00004101174,0.00001920765,0.00008253322,0.00004916042],"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":[0.00003711222,0.00006227872,0.0007161213,0.00009494994,0.00005391265,0.0003746017,0.000174774,0.9152784,0.001006921,0.07223761,0.001270914,0.008692432],"study_design_scores_gemma":[0.000007863926,0.00002362905,0.000102023,0.00001260005,0.00001971873,0.00004178366,0.0000489618,0.9884107,0.00008755387,0.009320132,0.00191442,0.00001066767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04721971,0.003880864,0.8993143,0.002600983,0.0006245315,0.0003103195,0.0005280363,0.0004942602,0.04502709],"genre_scores_gemma":[0.9191435,0.00349217,0.06085901,0.000350493,0.0002572155,0.000434723,0.0002481741,0.00007012334,0.01514463],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02279613,"threshold_uncertainty_score":0.04532689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03324201512342086,"score_gpt":0.2705022153401982,"score_spread":0.2372602002167774,"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."}}