{"id":"W7098029336","doi":"","title":"Sequential Design for Computer Experiments with a Flexible Bayesian Additive Model.” Canadian","year":2012,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Design of experiments; Sequential analysis; Sequential estimation; Bayesian experimental design","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06787515,0.001848851,0.002380745,0.001291899,0.001016068,0.001816112,0.002481363,0.002120308,0.01363254],"category_scores_gemma":[0.1034157,0.002425589,0.002146653,0.00141415,0.002814727,0.002052364,0.002277472,0.003187767,0.00162048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00332814,"about_ca_system_score_gemma":0.006692354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006410964,"about_ca_topic_score_gemma":0.009416449,"domain_scores_codex":[0.9064818,0.08418053,0.001362392,0.002689877,0.004571157,0.0007142059],"domain_scores_gemma":[0.9273564,0.06075067,0.002890436,0.005469534,0.002879541,0.0006534458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00836558,0.0008615847,0.002431773,0.002538016,0.001737499,0.0001418538,0.0008482748,0.1356871,0.004589751,0.3705939,0.01528517,0.4569195],"study_design_scores_gemma":[0.003634131,0.002553421,0.003336817,0.0004563689,0.0008388675,0.0001076503,0.00008871525,0.6310575,0.005155224,0.317006,0.03555749,0.0002079163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003012092,0.0004510128,0.991877,0.0003443383,0.0001968681,0.001313684,0.0001301509,0.0003408384,0.002334004],"genre_scores_gemma":[0.04657128,0.0003302956,0.9460576,0.0002354141,0.00008589678,0.00480795,0.0001088468,0.00007225443,0.001730506],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06787515,"threshold_uncertainty_score":0.3589624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2949075155898438,"score_gpt":0.455892094913676,"score_spread":0.1609845793238321,"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."}}