{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005267479,0.00009632019,0.0001042234,0.00007658618,0.00003240122,0.0000288155,0.00006639695,0.00003744812,0.0006401921],"category_scores_gemma":[0.000007272442,0.0000673374,0.00007290995,0.0001290129,0.00001239324,0.00009812522,0.00001792242,0.00003958416,0.0003836199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002128801,"about_ca_system_score_gemma":0.000003109582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001228736,"about_ca_topic_score_gemma":0.00001873094,"domain_scores_codex":[0.9994039,0.00000692807,0.000158699,0.0001233102,0.00008862112,0.000218533],"domain_scores_gemma":[0.9997477,0.000022966,0.00001365427,0.0001186432,0.00002828907,0.00006874221],"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":[6.285443e-7,0.0000118194,0.0006345196,0.00001887536,0.00004775861,0.000003498585,0.00004480384,0.003790816,0.02167489,0.00006364952,0.0001843136,0.9735245],"study_design_scores_gemma":[0.0009946142,0.00003045321,0.004215092,0.0001777085,0.0001065229,0.0000283084,0.0001027696,0.9282733,0.0481877,0.004910836,0.0120253,0.0009474515],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1988467,0.00006837708,0.7869815,0.0000723978,0.00007439901,0.00005514046,8.093417e-7,0.0005604368,0.01334025],"genre_scores_gemma":[0.9792393,0.00003683372,0.01955346,0.00003664326,0.00005901839,0.00001640289,0.000001140387,0.00002186123,0.001035373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.972577,"threshold_uncertainty_score":0.7009654,"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."}}