{"id":"W2559620539","doi":"10.1109/cec.2016.7743823","title":"Smartphone gait fingerprinting models via genetic programming","year":2016,"lang":"en","type":"article","venue":"","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Genetic programming; Biometrics; Gait; Authentication (law); Accelerometer; Identification (biology); Fingerprint (computing); Phone; Modular design; Genetic algorithm; Artificial intelligence; Data mining; Machine learning; Computer security","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0002806305,0.0004094921,0.0002811385,0.0003939776,0.0001710384,0.000420148,0.0004319631,0.0004261931,0.0009918535],"category_scores_gemma":[0.001275276,0.0001982921,0.0004985784,0.0002861057,0.0002739927,0.0002627779,0.0002575112,0.0003971016,0.0002045219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004077284,"about_ca_system_score_gemma":0.0004556689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01066354,"about_ca_topic_score_gemma":0.006805385,"domain_scores_codex":[0.9998977,0.00002827412,0.000004657071,0.00002847241,0.00002863752,0.00001228585],"domain_scores_gemma":[0.9997469,0.000135318,0.00003415891,0.00001907479,0.00005576498,0.000008712942],"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":[0.00001257132,0.00001028685,0.000435798,0.000007458753,0.00000767614,0.00001832986,0.0000183127,0.9879083,0.00101235,0.001516284,0.00009528521,0.008957434],"study_design_scores_gemma":[7.740401e-7,0.00000462228,0.00006802978,0.000001032275,0.000001325039,0.000003292588,0.000001736592,0.9993249,0.0001681896,0.0003547877,0.00007006802,0.000001283157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1368775,0.0001025997,0.858149,0.0001644104,0.00002584476,0.00006048793,0.0001513076,0.0005700256,0.003898745],"genre_scores_gemma":[0.8744798,0.0001405382,0.1209254,0.00003832225,0.00001060562,0.0001638746,0.0001817819,0.0000632369,0.003996621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01066354,"threshold_uncertainty_score":0.02120292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01035548241611806,"score_gpt":0.1819512453022986,"score_spread":0.1715957628861805,"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."}}