{"id":"W2119053066","doi":"10.1109/icassp.2004.1327162","title":"Dirichlet-based probability model applied to human skin detection [image skin detection]","year":2004,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Dirichlet distribution; Latent Dirichlet allocation; Probabilistic logic; Artificial intelligence; Pattern recognition (psychology); Computer science; Generalization; Statistical model; Mixture model; Probability model; Topic model; Mathematics; Statistics","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":[0.002606346,0.001003389,0.002015542,0.001911055,0.0009065485,0.001587829,0.002541863,0.002237601,0.003574846],"category_scores_gemma":[0.01114531,0.001180217,0.002001723,0.002390085,0.001643567,0.002570308,0.001532501,0.002670841,0.002108968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001152436,"about_ca_system_score_gemma":0.001070673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004102401,"about_ca_topic_score_gemma":0.002571066,"domain_scores_codex":[0.9975096,0.001187546,0.0001131214,0.0004633433,0.0006041866,0.0001222491],"domain_scores_gemma":[0.9973929,0.001790038,0.0001114552,0.0003208163,0.0003126684,0.00007215851],"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":[0.0001988983,0.0001063186,0.0007538494,0.0003547009,0.0001791093,0.0003328567,0.0003844122,0.586546,0.01027985,0.1927908,0.007076336,0.2009969],"study_design_scores_gemma":[0.000007567495,0.00001846691,0.0001913051,0.00001863631,0.00001768953,0.0001639219,0.00001178075,0.9267971,0.001823836,0.06802393,0.002890446,0.00003532004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008693532,0.0002097917,0.9979569,0.00008879651,0.00004537436,0.00001898834,0.00003070086,0.0002032537,0.0005768737],"genre_scores_gemma":[0.2194369,0.002255345,0.7656604,0.0003268663,0.0004341021,0.0004598645,0.0006094659,0.000465195,0.0103519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004102401,"threshold_uncertainty_score":0.01378387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01788572812261474,"score_gpt":0.2649211283766104,"score_spread":0.2470354002539957,"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."}}