{"id":"W2890477142","doi":"10.1109/isc2.2018.8656903","title":"Mobility Mode Detection Using WiFi Signals","year":2018,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Decision tree; Random forest; Computer science; Mode (computer interface); Multilayer perceptron; Tree (set theory); Perceptron; Artificial intelligence; Artificial neural network; Downtown; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009337619,0.00005234246,0.00008329704,0.00007359613,0.0008834083,0.00005956759,0.0001072979,0.00006681665,0.00267882],"category_scores_gemma":[0.0001886821,0.00004984807,0.00006555904,0.0004764461,0.0004028576,0.0001656421,0.00001108433,0.00004912796,0.0001274841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001386068,"about_ca_system_score_gemma":0.0001315673,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05096263,"about_ca_topic_score_gemma":0.1185663,"domain_scores_codex":[0.9990607,0.0002147568,0.0001442139,0.000191803,0.0002190216,0.0001694944],"domain_scores_gemma":[0.9994199,0.00007781997,0.00003941253,0.0001727599,0.0002139604,0.00007609845],"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":[0.0002096744,0.002091459,0.04646492,0.0001066163,0.0004101712,0.000003105942,0.1372778,0.01710217,0.2351486,0.07292365,0.0009680726,0.4872938],"study_design_scores_gemma":[0.0006148266,0.000312531,0.01166178,0.0000417881,0.0002859843,7.626714e-7,0.03130329,0.7119659,0.1204771,0.0733115,0.04888123,0.001143369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8641787,0.000007607363,0.1064707,0.0001592883,0.00008194455,0.0001118717,0.000001123338,0.00008716853,0.02890156],"genre_scores_gemma":[0.997511,0.000002019569,0.0003047248,0.000145945,0.0003658217,0.000005204406,9.023032e-7,0.000003192322,0.001661178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6948637,"threshold_uncertainty_score":0.9982328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04890299641620003,"score_gpt":0.3679297364371561,"score_spread":0.319026740020956,"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."}}