{"id":"W2889272453","doi":"10.1039/c8sc02239a","title":"Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories","year":2018,"lang":"en","type":"article","venue":"Chemical Science","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":163,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto; Canadian Institute for Advanced Research","funders":"Tata Sons; FAS Division of Science, Harvard University; Harvard University","keywords":"Chimera (genetics); Computer science; Interpretability; A priori and a posteriori; Multi-objective optimization; Mathematical optimization; Artificial intelligence; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005523143,0.000227385,0.0001997133,0.0002216994,0.0007586118,0.000341494,0.001225204,0.00008401699,0.00001765825],"category_scores_gemma":[0.001687657,0.0002165852,0.00005522349,0.00316102,0.0008047121,0.001816726,0.0003557857,0.0001452504,0.00001038699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003674529,"about_ca_system_score_gemma":0.0005533721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003065263,"about_ca_topic_score_gemma":0.000001318691,"domain_scores_codex":[0.9975287,0.00003355005,0.0002786318,0.00101804,0.0004981526,0.0006428827],"domain_scores_gemma":[0.9971504,0.0002621935,0.0001696009,0.0004593294,0.001697817,0.0002606927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007115817,0.001048382,0.00129192,0.00007730538,0.00005143416,0.000008405434,0.00576337,0.1101672,0.8261968,0.0238845,0.0001541529,0.03128527],"study_design_scores_gemma":[0.0004795784,0.00004737205,0.00004956407,0.00001500047,0.000003252915,0.00000181668,0.00002479038,0.6665193,0.3321093,0.0001590789,0.000385124,0.0002058409],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001757414,0.00002642765,0.996126,0.000191906,0.0005111722,0.0004578517,0.000005594417,0.0005204869,0.0004031325],"genre_scores_gemma":[0.1968819,0.000003993386,0.8025752,0.0002471721,0.0001752654,0.00007083868,0.000003744234,0.00001616555,0.00002576545],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5563521,"threshold_uncertainty_score":0.8832089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01339126336610078,"score_gpt":0.2846620514620578,"score_spread":0.2712707880959571,"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."}}