{"id":"W4398393146","doi":"10.7910/dvn/atxytp","title":"Facelift Beauty Classifier (Binary)","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Beauty; Binary number; Classifier (UML); Pattern recognition (psychology); Computer science; Artificial intelligence; Mathematics; Art; Arithmetic; Aesthetics","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":[0.0007169737,0.00460378,0.001768536,0.001992843,0.00129116,0.001619376,0.004166015,0.002616256,0.03280951],"category_scores_gemma":[0.002134678,0.0006830459,0.001806765,0.001928097,0.0005143326,0.001227419,0.001925064,0.002471782,0.05620124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001376954,"about_ca_system_score_gemma":0.001282948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02241869,"about_ca_topic_score_gemma":0.06022779,"domain_scores_codex":[0.9991947,0.00009383806,0.00004634034,0.0002364729,0.0002724066,0.0001563013],"domain_scores_gemma":[0.999366,0.00009124146,0.00004060505,0.0002237136,0.0001903325,0.00008802348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002222764,0.0001504594,0.0007342288,0.0004257643,0.0000619006,0.00007326836,0.0000204302,0.0007779338,0.0008278094,0.0003351708,0.9801139,0.0162569],"study_design_scores_gemma":[0.0007772732,0.0004080742,0.01263829,0.0004695678,0.0002042346,0.001338255,0.0001818178,0.01944546,0.009699052,0.003484384,0.951197,0.0001567061],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01089064,0.001871153,0.003189362,0.000545313,0.0007147741,0.0003440529,0.9582996,0.01330432,0.01084068],"genre_scores_gemma":[0.004888278,0.0002091124,0.002944981,0.0001781613,0.00004006574,0.000219862,0.9865224,0.0003743268,0.004622723],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03280951,"threshold_uncertainty_score":0.1097588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02867018697340816,"score_gpt":0.251603533398208,"score_spread":0.2229333464247998,"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."}}