{"id":"W3172457552","doi":"10.1063/5.0048164","title":"G<scp>ryffin</scp>: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge","year":2021,"lang":"en","type":"article","venue":"Applied Physics Reviews","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":156,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Vector Institute; University of Toronto","funders":"Office of Naval Research","keywords":"Categorical variable; Bayesian optimization; Domain (mathematical analysis); Process (computing); Kernel (algebra); Domain knowledge; Bayesian probability; Identification (biology)","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":[0.002654646,0.001707204,0.001686568,0.001364639,0.0008361883,0.001556281,0.003088006,0.003741215,0.01451831],"category_scores_gemma":[0.009000473,0.001127454,0.001515582,0.00174202,0.001563483,0.001737382,0.002799474,0.003306587,0.004224029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002079971,"about_ca_system_score_gemma":0.003657083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01806153,"about_ca_topic_score_gemma":0.02220749,"domain_scores_codex":[0.9988841,0.0004385807,0.00005186913,0.0002246216,0.0002748523,0.0001259414],"domain_scores_gemma":[0.9973221,0.001862175,0.0001308687,0.0002247388,0.0003599628,0.0001000836],"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":[0.0002233329,0.0001332058,0.001032728,0.00027907,0.000122202,0.0001539794,0.0001052015,0.6505821,0.001653378,0.04223981,0.02703305,0.2764419],"study_design_scores_gemma":[0.00003014114,0.00001462099,0.00005326256,0.00001819112,0.000005344153,0.00001275198,0.000006479426,0.9857072,0.0003638763,0.01166436,0.002115981,0.000007610385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001992779,0.0001654534,0.9928868,0.0003559333,0.00006457498,0.0001109643,0.000197221,0.001768182,0.002458104],"genre_scores_gemma":[0.08120415,0.0001904266,0.9090843,0.0008032346,0.0001043603,0.0008342281,0.0009367745,0.001459105,0.005383465],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01806153,"threshold_uncertainty_score":0.04856861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02413911206244987,"score_gpt":0.3084115666260863,"score_spread":0.2842724545636364,"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."}}