{"id":"W3010324225","doi":"10.1109/globecom38437.2019.9014252","title":"Push vs Pull Participant Recruitment System for Personalized Vehicular Crowdsensing","year":2019,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Crowdsensing; Computer science; Focus (optics); Crowdsourcing; Participatory sensing; Real-time computing; Distributed computing; Computer security; Data science; World Wide Web","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":[0.003375363,0.001262124,0.001978932,0.001190163,0.002333581,0.001539327,0.002926266,0.001705705,0.004253062],"category_scores_gemma":[0.004790907,0.0005273392,0.00068182,0.0006255786,0.0006383543,0.001719631,0.004386031,0.0009586229,0.001683231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000651478,"about_ca_system_score_gemma":0.001849144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002518157,"about_ca_topic_score_gemma":0.003294918,"domain_scores_codex":[0.9978296,0.0005556191,0.0001649573,0.0005974742,0.0005371766,0.000315118],"domain_scores_gemma":[0.9967969,0.00122306,0.0001873654,0.0004461551,0.0006250475,0.0007215217],"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":[0.01016445,0.003170284,0.03195172,0.001385439,0.0005801393,0.003598351,0.003777055,0.06090965,0.1821832,0.02079086,0.02808155,0.6534073],"study_design_scores_gemma":[0.0007271173,0.001531539,0.004360443,0.00004764994,0.0002208484,0.001198436,0.001623895,0.9205692,0.03643711,0.0136927,0.01934435,0.00024671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2914798,0.001069724,0.6794149,0.002092596,0.0009742242,0.002891124,0.0009582457,0.01047054,0.01064894],"genre_scores_gemma":[0.8941172,0.0001570659,0.09859923,0.0004612497,0.0001734862,0.000833019,0.0004453577,0.0001301418,0.005083218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004253062,"threshold_uncertainty_score":0.01785088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07349419926790775,"score_gpt":0.2831627236717175,"score_spread":0.2096685244038098,"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."}}