{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003144899,0.0004160072,0.0005233595,0.0003925407,0.0001391007,0.0004184722,0.00263474,0.0003651211,0.02542204],"category_scores_gemma":[0.0001027591,0.0003895197,0.0003063261,0.0004963941,0.00006722748,0.000802358,0.001359241,0.000562341,0.553568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009159013,"about_ca_system_score_gemma":0.0002358066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001101036,"about_ca_topic_score_gemma":0.0000536495,"domain_scores_codex":[0.9973534,0.0001366691,0.0003846333,0.0009666858,0.0006326056,0.0005259591],"domain_scores_gemma":[0.996264,0.00008267609,0.0002643116,0.003026256,0.000109973,0.0002527491],"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":[0.000003715828,0.00007952607,0.000002686111,0.00006202299,0.00009539147,0.00008246593,0.00001151448,0.00001688621,0.00001616616,0.00004916402,0.9987289,0.0008515712],"study_design_scores_gemma":[0.0003412395,0.00003932273,0.0000118175,0.00006885058,0.000103992,0.00001043301,0.00002025676,0.003118258,0.00002230129,0.00002637857,0.9957397,0.0004974225],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000002702052,0.000002700373,0.004953668,0.00007941881,0.001314229,0.0002056451,0.9927575,0.0001118248,0.0005723002],"genre_scores_gemma":[0.000004401131,0.0005026502,0.001857855,0.001766049,0.0001287558,0.00001710277,0.992405,0.00001527006,0.00330289],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.528146,"threshold_uncertainty_score":0.9998557,"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."}}