{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004667718,0.000145812,0.0003117829,0.0002188426,0.001140102,0.0003105056,0.0005576666,0.0002082196,0.00004194196],"category_scores_gemma":[0.0006145682,0.0001447425,0.00007326531,0.0004248972,0.0002196879,0.0003096655,0.0004513547,0.000171069,0.000003955212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002252039,"about_ca_system_score_gemma":0.0003009638,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01919252,"about_ca_topic_score_gemma":0.00591374,"domain_scores_codex":[0.998049,0.0004317626,0.0003508634,0.0007316079,0.0001937478,0.0002430862],"domain_scores_gemma":[0.9982285,0.0005388414,0.0001946725,0.0007463458,0.000202002,0.00008962599],"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":[0.0001565707,0.0002407189,0.1248377,0.01098353,0.001770239,0.000004052327,0.1797211,0.1027439,0.001485521,0.08876363,0.001825159,0.4874678],"study_design_scores_gemma":[0.00008439612,0.000009043948,0.0008921523,0.0002304645,0.000220888,3.052935e-7,0.03196229,0.9577373,0.00009354737,0.0002296186,0.008249044,0.0002910035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1677954,0.0001375786,0.8301609,0.0001633604,0.0004665599,0.0005309114,0.00002870476,0.0002163382,0.000500266],"genre_scores_gemma":[0.9286733,0.0001585945,0.0685612,0.00002647631,0.0003815588,0.0001119376,0.0001444518,0.00001893214,0.001923558],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8549933,"threshold_uncertainty_score":0.9873388,"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."}}