{"id":"W4318192376","doi":"10.31219/osf.io/sv6cg","title":"Methods for Analyzing System Performance and User Experience Using WiFi Connection Data","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Bluetooth; Reliability (semiconductor); Computer science; Data collection; Public transport; Service (business); Measure (data warehouse); Transit (satellite); Automatic vehicle location; Transport engineering; Real-time computing; Telecommunications; Wireless; Database; Global Positioning System; Engineering","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":[0.005459327,0.00161712,0.0007888877,0.005010907,0.0005765241,0.002105068,0.001446586,0.001098906,0.001730136],"category_scores_gemma":[0.02583338,0.0005399668,0.001260416,0.004718889,0.0006703573,0.001967607,0.001142618,0.001221074,0.0009846714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008400165,"about_ca_system_score_gemma":0.001008899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009361085,"about_ca_topic_score_gemma":0.007480949,"domain_scores_codex":[0.9942558,0.00186587,0.0005166584,0.00116446,0.001956912,0.0002402605],"domain_scores_gemma":[0.9822903,0.01109946,0.002259496,0.002366505,0.001800134,0.0001840096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004259686,0.0007196977,0.1391266,0.001048165,0.001215398,0.0001652131,0.001396492,0.1685184,0.01194122,0.01978231,0.007132778,0.6485279],"study_design_scores_gemma":[0.00005968702,0.0003393356,0.07518294,0.0001558243,0.0001416873,0.0002726555,0.0006066897,0.8811015,0.008863066,0.02150258,0.01161659,0.000157352],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03004312,0.0004637559,0.9630123,0.000149553,0.00004149165,0.000443635,0.002570631,0.001351367,0.001924213],"genre_scores_gemma":[0.3569238,0.0008836785,0.6294514,0.0001115423,0.0002238645,0.002685835,0.006318667,0.0003380485,0.00306314],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009361085,"threshold_uncertainty_score":0.02887201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2899008600825726,"score_gpt":0.4939299781321248,"score_spread":0.2040291180495522,"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."}}