{"id":"W6995888774","doi":"","title":"Practical Bayesian Optimization of Machine Learning Algorithms","year":2014,"lang":"en","type":"article","venue":"Digital Access to Scholarship at Harvard (DASH) (Harvard University)","topic":"Esophageal Cancer Research and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Nucleofection; Articular cartilage damage; Process (computing); Proteogenomics; Tubulopathy; Gestational period","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":[0.01154704,0.001790342,0.002046021,0.001247041,0.001148251,0.002903836,0.002202437,0.002705996,0.004598056],"category_scores_gemma":[0.04369046,0.001638137,0.001110156,0.001232941,0.002679465,0.003696873,0.003072541,0.00388899,0.001705133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002887355,"about_ca_system_score_gemma":0.003468312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005000548,"about_ca_topic_score_gemma":0.00484452,"domain_scores_codex":[0.9935693,0.003663437,0.0002954257,0.0008875051,0.001247814,0.0003366394],"domain_scores_gemma":[0.9845367,0.01222762,0.0005617841,0.00100231,0.00144845,0.0002231773],"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":[0.00008290965,0.00006447558,0.0006183158,0.0002085514,0.00005322288,0.00004250649,0.0001200283,0.7926868,0.0009121049,0.1512784,0.003296278,0.05063647],"study_design_scores_gemma":[0.00001711584,0.00001514977,0.00008225064,0.00002839723,0.000005337133,0.0000124007,0.00001160081,0.9148332,0.000393432,0.08325958,0.001330587,0.00001102149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002696935,0.0002854437,0.9931391,0.0004021543,0.00002673418,0.00004431192,0.00003668032,0.0002629493,0.003105682],"genre_scores_gemma":[0.2343775,0.0007983227,0.7575316,0.0004777867,0.0001478193,0.0006197426,0.0003184127,0.0005505023,0.005178316],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01154704,"threshold_uncertainty_score":0.06106734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03503834716608992,"score_gpt":0.308941627981219,"score_spread":0.273903280815129,"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."}}