{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001123911,0.0002217267,0.0003140411,0.0003143133,0.001048215,0.0001100315,0.0005969188,0.0004679973,0.0007126793],"category_scores_gemma":[0.0001962937,0.0002826082,0.0003335587,0.0008782718,0.0007599379,0.0002320014,0.0002439923,0.0004177304,0.0001050619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000913448,"about_ca_system_score_gemma":0.000626729,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04007415,"about_ca_topic_score_gemma":0.03373403,"domain_scores_codex":[0.9976305,0.0007120955,0.000233669,0.0009071523,0.0001647032,0.0003519054],"domain_scores_gemma":[0.9982191,0.0001524176,0.0002626238,0.0007150734,0.0004500732,0.0002006615],"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":[0.00009391271,0.000375695,0.009165104,0.0001332045,0.0002792339,0.00002239383,0.005152131,0.9722008,0.0005077353,0.0104443,0.00006018863,0.001565283],"study_design_scores_gemma":[0.0002778382,0.00004794993,0.001426019,0.0001031455,0.0005938535,2.480824e-7,0.00333207,0.8915129,0.0007229225,0.09959506,0.001633745,0.0007542592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8384212,0.00001966839,0.1564277,0.00003133058,0.0002687763,0.0003089029,0.00002114519,0.0001435884,0.004357654],"genre_scores_gemma":[0.997633,0.00005551433,0.0000699758,0.00004110738,0.0003713101,0.000001279776,0.00001641781,0.00001367002,0.001797678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1592118,"threshold_uncertainty_score":0.9999626,"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."}}