{"id":"W2113955139","doi":"10.1145/1569901.1569940","title":"An experimental investigation of model-based parameter optimisation","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Gaussian process; Process (computing); Key (lock); Model parameter; Gaussian network model; Machine learning; Gaussian; Artificial intelligence; Mathematical optimization; Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004720539,0.001179026,0.001083081,0.0006790293,0.0004594261,0.001386594,0.002138199,0.001995994,0.004154448],"category_scores_gemma":[0.02655984,0.0004670978,0.0006156024,0.000762663,0.001171318,0.002075011,0.001653059,0.001927072,0.00109339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007614144,"about_ca_system_score_gemma":0.0007411555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008322956,"about_ca_topic_score_gemma":0.0005149493,"domain_scores_codex":[0.9959026,0.001546205,0.0002950235,0.0008100843,0.001141364,0.0003047229],"domain_scores_gemma":[0.988295,0.007155,0.0007984369,0.002532677,0.001046285,0.0001726552],"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":[0.003688298,0.002349812,0.003205035,0.001553882,0.0002497843,0.0002245741,0.0005004738,0.5963547,0.2248573,0.01909271,0.002547989,0.1453754],"study_design_scores_gemma":[0.0002976946,0.004452925,0.002072174,0.00009600368,0.00007694701,0.0002415135,0.0001409929,0.7833442,0.1935845,0.01017235,0.005378156,0.0001425608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4379256,0.0007372605,0.5477072,0.0007009116,0.0002400992,0.0006070795,0.0004820901,0.002184683,0.009415032],"genre_scores_gemma":[0.8878068,0.0001588229,0.1092325,0.000190707,0.00002328779,0.0004470586,0.0002565359,0.0002508099,0.001633366],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004720539,"threshold_uncertainty_score":0.02496487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02873990815601129,"score_gpt":0.3002304423346162,"score_spread":0.2714905341786049,"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."}}