{"id":"W2099988552","doi":"10.1007/s13177-014-0078-z","title":"VANET IR-CAS for Commercial SA: Information Retrieval Context Aware System for VANET Commercial Service Announcement","year":2014,"lang":"en","type":"article","venue":"International Journal of Intelligent Transportation Systems Research","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Vehicular ad hoc network; Computer science; Relevance (law); Context (archaeology); Scalability; Service (business); Ontology; Exploit; Computer network; Computer security; Wireless ad hoc network; Database; Telecommunications; 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":[0.000677623,0.001047395,0.001146597,0.001751398,0.0006631384,0.001476225,0.001980286,0.0009054672,0.01182284],"category_scores_gemma":[0.001853034,0.0004594469,0.000433355,0.001091782,0.0002061237,0.001483061,0.0009263434,0.0008873626,0.007168009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005363309,"about_ca_system_score_gemma":0.00106775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006548645,"about_ca_topic_score_gemma":0.006855959,"domain_scores_codex":[0.9995895,0.0000612542,0.00003971672,0.0001262461,0.0001131999,0.00007017767],"domain_scores_gemma":[0.9993191,0.00008691669,0.00004850869,0.0002028367,0.0002302271,0.0001124631],"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":[0.005126342,0.001202936,0.008936829,0.000891647,0.0005013824,0.0007649479,0.0003789413,0.007136414,0.1396677,0.005618436,0.3143669,0.5154074],"study_design_scores_gemma":[0.0006539749,0.001373466,0.01011871,0.00009448665,0.0005043233,0.001203827,0.0003231753,0.6162753,0.2101971,0.00402044,0.1547361,0.0004990246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"empirical","genre_scores_codex":[0.1204038,0.004314731,0.2842074,0.0009279355,0.001049714,0.001911355,0.01770271,0.5357632,0.03371924],"genre_scores_gemma":[0.693612,0.0008156953,0.2448844,0.0009476279,0.0003473347,0.0007240292,0.02754343,0.003031405,0.02809403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01182284,"threshold_uncertainty_score":0.03955138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05209184803754505,"score_gpt":0.3289881589939135,"score_spread":0.2768963109563684,"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."}}