{"id":"W2043630098","doi":"10.1145/1413634.1413668","title":"Biometric tendency recognition and classification system","year":2008,"lang":"en","type":"article","venue":"","topic":"Aesthetic Perception and Analysis","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Biometrics; Computer science; Relation (database); Context (archaeology); Biometric data; Software; Contextual image classification; Statistical classification; Artificial intelligence; Computer security; Pattern recognition (psychology); Image (mathematics); Data mining","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":[0.001644306,0.0005034054,0.0009291021,0.001976384,0.000592791,0.001160494,0.0009943476,0.001083366,0.01675639],"category_scores_gemma":[0.004878928,0.0002463507,0.0003418428,0.001341842,0.0003420545,0.001008619,0.0008614464,0.0005831312,0.01269387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000596646,"about_ca_system_score_gemma":0.0007922114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001702129,"about_ca_topic_score_gemma":0.001250024,"domain_scores_codex":[0.9984585,0.0002210789,0.0001678603,0.0004606028,0.0005756371,0.0001164139],"domain_scores_gemma":[0.9972791,0.0003745041,0.0002705198,0.0003512774,0.001573212,0.0001515237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001700573,0.000440931,0.02024669,0.0003467028,0.0000984385,0.0003652014,0.0003754687,0.004024181,0.137431,0.01019056,0.04248265,0.7822976],"study_design_scores_gemma":[0.0004481747,0.001729498,0.1210879,0.0001857607,0.0002804536,0.005837097,0.0004389579,0.4639436,0.2331488,0.01601874,0.1562027,0.0006783726],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1005765,0.0005256125,0.8002961,0.0009926299,0.0007645196,0.003015122,0.007185905,0.04501467,0.04162889],"genre_scores_gemma":[0.5582055,0.0003903777,0.3876132,0.001066593,0.0002658287,0.003535462,0.005128496,0.0006365207,0.04315798],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01675639,"threshold_uncertainty_score":0.05605567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1927884471915483,"score_gpt":0.2843481124252893,"score_spread":0.09155966523374101,"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."}}