{"id":"W2951791712","doi":"10.48550/arxiv.1809.05788","title":"Mobility Mode Detection Using WiFi Signals","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Decision tree; Random forest; Mode (computer interface); Computer science; Multilayer perceptron; Tree (set theory); Perceptron; Downtown; Artificial neural network; Artificial intelligence; Real-time computing; Machine learning; Data mining; Pattern recognition (psychology); Geography; Human–computer interaction; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001396843,0.0006924394,0.0002423814,0.0009343954,0.0001810222,0.0003355079,0.0003676676,0.0003749934,0.00108577],"category_scores_gemma":[0.0009927328,0.0001252192,0.0003034258,0.0005327589,0.0001116683,0.0003727148,0.0003999265,0.00027765,0.0005897965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003013561,"about_ca_system_score_gemma":0.0003074369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02386058,"about_ca_topic_score_gemma":0.03189965,"domain_scores_codex":[0.9998658,0.00001830852,0.000006196875,0.00004040592,0.00002631572,0.00004300187],"domain_scores_gemma":[0.999827,0.00004289511,0.00002651876,0.00001774474,0.0000583738,0.00002757578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008194232,0.0003658971,0.5525575,0.0002919754,0.0002871471,0.0008369518,0.0002805139,0.126157,0.0250915,0.001648702,0.01047403,0.2811894],"study_design_scores_gemma":[0.00001995686,0.0002384796,0.2197486,0.00005120043,0.00009480333,0.0004054815,0.0002067754,0.7672153,0.00835756,0.001234912,0.002380894,0.00004609544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9667755,0.0003005289,0.02397486,0.0002619285,0.00008856184,0.00005124819,0.003730601,0.0006230472,0.004193821],"genre_scores_gemma":[0.9948608,0.00009487442,0.003046587,0.00002021687,0.00001317374,0.000013715,0.00118332,0.000004706832,0.0007625206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02386058,"threshold_uncertainty_score":0.04744339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1239255367359181,"score_gpt":0.2567888069092911,"score_spread":0.132863270173373,"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."}}