{"id":"W2374120850","doi":"","title":"D-optimal Orthogonal Block Designs with Parameter Estimation for the Additive Mixture Models","year":2009,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Orthogonal array; Block (permutation group theory); Mathematics; Block design; Orthogonal matrix; Orthogonal transformation; Applied mathematics; Optimal design; Mathematical optimization; Orthogonal basis; Combinatorics; Algorithm; Statistics; Taguchi methods","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":[0.002006601,0.0002384356,0.0002810246,0.0001144698,0.0002992588,0.0003723487,0.0006387988,0.0001028471,0.0003815345],"category_scores_gemma":[0.0009886975,0.0001134024,0.0001591306,0.0005056994,0.0001529058,0.0006945662,0.00003934897,0.0001527863,0.00005993458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003820587,"about_ca_system_score_gemma":0.00008866176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004216396,"about_ca_topic_score_gemma":0.000003233048,"domain_scores_codex":[0.9972708,0.000250446,0.0004405468,0.0005687515,0.001118035,0.0003514607],"domain_scores_gemma":[0.9928462,0.005952179,0.0001817353,0.0005293478,0.0003699295,0.0001206261],"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":[0.001602354,0.0002662778,0.00002936127,0.000001686485,0.00008782052,0.00001001619,0.001396303,0.6302984,0.004729246,0.05128223,0.02848613,0.2818102],"study_design_scores_gemma":[0.0005455562,0.0009749262,0.0004725966,0.000008281489,0.00003487347,0.00003450786,0.0005467614,0.9164619,0.0157862,0.06406015,0.0008400498,0.0002341986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01018887,0.00008862581,0.9826608,0.00121636,0.00008361018,0.000910822,0.00003655106,0.00006435761,0.004750003],"genre_scores_gemma":[0.4031346,0.000001758767,0.5943022,0.0009159333,0.00004272677,0.00007304514,0.000005111796,0.0000101178,0.001514569],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3929457,"threshold_uncertainty_score":0.4624415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1651907493316478,"score_gpt":0.4211642392842557,"score_spread":0.2559734899526079,"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."}}