{"id":"W2030206951","doi":"10.1016/j.ins.2012.09.011","title":"Computerized facial diagnosis using both color and texture features","year":2012,"lang":"en","type":"article","venue":"Information Sciences","topic":"Traditional Chinese Medicine Studies","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Hong Kong Polytechnic University","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision; Face (sociological concept); Feature (linguistics); Texture (cosmology); Image (mathematics)","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":[0.0003896143,0.0003164188,0.0003469345,0.0030228,0.0001766564,0.0006145841,0.0002144325,0.0003487839,0.003998969],"category_scores_gemma":[0.00123979,0.0001488111,0.0004051736,0.001117181,0.0001983593,0.0006592454,0.0003741238,0.000241705,0.0006507015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002826147,"about_ca_system_score_gemma":0.0004558553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004237643,"about_ca_topic_score_gemma":0.004455984,"domain_scores_codex":[0.9998119,0.00003118765,0.00001735615,0.00003918154,0.00007811391,0.00002223265],"domain_scores_gemma":[0.9995775,0.0001543599,0.00004359673,0.00003460771,0.000169048,0.00002092149],"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":[0.001288386,0.0001711516,0.07575602,0.0003467734,0.0001437469,0.0007952388,0.000133688,0.003685944,0.1735046,0.001250826,0.002350987,0.7405726],"study_design_scores_gemma":[0.0002496261,0.00100205,0.4633296,0.0001959308,0.0008482215,0.01010282,0.001116107,0.3548259,0.153972,0.004635151,0.009569766,0.0001529631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7319758,0.001785683,0.2461271,0.0004782807,0.0001516404,0.0005172897,0.002415289,0.001294372,0.01525452],"genre_scores_gemma":[0.8787809,0.001354067,0.1149232,0.0001105006,0.00008080759,0.0001261231,0.0007025939,0.00004813925,0.003873641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004237643,"threshold_uncertainty_score":0.01337785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05299098114516179,"score_gpt":0.3363821884724308,"score_spread":0.283391207327269,"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."}}