{"id":"W1007063632","doi":"","title":"Dealing with data complexity: on neural networks and fusion in biometric research","year":2010,"lang":"en","type":"article","venue":"Journal of Medical Informatics & Technologies","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Biometrics; Computer science; Artificial neural network; Sensor fusion; Artificial intelligence; Biometric data","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":[0.005620463,0.00007770741,0.0001885989,0.002863919,0.0001145694,0.0002513716,0.002868127,0.0002483657,0.00000699427],"category_scores_gemma":[0.001846444,0.00004937161,0.0000144626,0.003829245,0.000513655,0.00064285,0.001327843,0.001997369,0.000002014189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002274173,"about_ca_system_score_gemma":0.00007394269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001806782,"about_ca_topic_score_gemma":0.00003992854,"domain_scores_codex":[0.9974901,0.00003807429,0.0005916936,0.00009890534,0.001548791,0.0002324],"domain_scores_gemma":[0.9983239,0.0004783443,0.0002820623,0.0006045843,0.0002122095,0.00009894802],"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":[0.0000143708,0.00009100206,0.002258842,0.00002819183,0.000009666291,0.00004196731,0.0002321557,0.00001950064,0.00001401103,0.04001471,0.0007504392,0.9565251],"study_design_scores_gemma":[0.0005507611,0.0002825075,0.003063813,0.0001081704,0.000002567225,0.0003676623,0.001018572,0.9857687,0.00006937394,0.003148204,0.005525261,0.00009438918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6296557,0.0006122672,0.3518653,0.01685215,0.000463184,0.0001716384,0.000002732568,0.0001789925,0.0001980027],"genre_scores_gemma":[0.9640084,0.000925538,0.03492682,0.0001094235,0.00002332636,7.180456e-7,0.00000187271,0.000002471959,0.000001439831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9857492,"threshold_uncertainty_score":0.8677686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1677678073598637,"score_gpt":0.3931490647114482,"score_spread":0.2253812573515845,"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."}}