{"id":"W2944214248","doi":"10.2478/jaiscr-2019-0001","title":"Score Level and Rank Level Fusion for Kinect-Based Multi-Modal Biometric System","year":2019,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence and Soft Computing Research","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Mitacs","keywords":"Biometrics; Artificial intelligence; Gait; Computer science; Pattern recognition (psychology); Feature (linguistics); Modality (human–computer interaction); Rank (graph theory); Modal; Face (sociological concept); Support vector machine; Logistic regression; Machine learning; Mathematics; Medicine; Physical medicine and rehabilitation","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002099671,0.0001220576,0.0003079259,0.001221432,0.0001974848,0.0001803333,0.0001564039,0.0000905995,0.00001676428],"category_scores_gemma":[0.0003197971,0.0001034704,0.0001127058,0.0008926899,0.00007259239,0.0000982534,0.00004492103,0.0003670258,0.00001987477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006407509,"about_ca_system_score_gemma":0.0000553868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002729001,"about_ca_topic_score_gemma":0.00001173086,"domain_scores_codex":[0.9984855,0.00007352423,0.0005381856,0.0001685854,0.0004134152,0.0003208103],"domain_scores_gemma":[0.998185,0.0008132583,0.0000946309,0.0000942895,0.0006621541,0.0001506316],"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":[0.0001857098,0.0001692269,0.006709056,0.001244856,0.0001531988,0.00002808184,0.0007143031,0.0485633,0.05199457,0.0004458542,0.0001875038,0.8896043],"study_design_scores_gemma":[0.0002038999,0.0002382828,0.002434807,0.000369594,0.00001969903,0.00002189636,0.001296995,0.9795329,0.01551042,0.0001426447,0.00009539719,0.0001334517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6198124,0.0003311512,0.3794264,0.0000572565,0.0001880667,0.0001364688,0.000008010365,0.00001937085,0.00002081543],"genre_scores_gemma":[0.9944628,0.00005149874,0.005232214,0.00001219054,0.0001889021,0.000001338354,0.000002702912,0.00001776296,0.00003061033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9309696,"threshold_uncertainty_score":0.4219399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2839608259157278,"score_gpt":0.3832731189089257,"score_spread":0.09931229299319794,"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."}}