{"id":"W2802818241","doi":"10.1002/cjs.11355","title":"Locally optimal designs for binary dose‐response models","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institutes of Health; Louisiana Clinical and Translational Science Center","keywords":"Binary number; Optimal design; Nonlinear system; Mathematical optimization; Algebraic number; Computer science; Mathematics; Algorithm; Applied mathematics; Statistics; Arithmetic; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.04678274,0.001287343,0.003267701,0.001839609,0.0005354129,0.001574779,0.002138538,0.002243856,0.003919918],"category_scores_gemma":[0.1096295,0.001225265,0.001668943,0.000845121,0.003826666,0.002179103,0.002879781,0.00312883,0.0005955084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002045527,"about_ca_system_score_gemma":0.001787721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005844168,"about_ca_topic_score_gemma":0.0004646874,"domain_scores_codex":[0.9445386,0.0482375,0.001163834,0.003121624,0.002306188,0.0006321936],"domain_scores_gemma":[0.8684536,0.1165445,0.006137016,0.005039689,0.003086642,0.0007385266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002940989,0.0005079981,0.00353436,0.001174595,0.0006449667,0.0001948402,0.0005823519,0.352221,0.008222396,0.5155664,0.001113062,0.113297],"study_design_scores_gemma":[0.0007392981,0.001692476,0.001659031,0.000189622,0.000179855,0.00008663246,0.00008962865,0.560823,0.004287808,0.4280824,0.002070387,0.00009983267],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01387406,0.0002138536,0.9847654,0.0001604826,0.0000162465,0.00019455,0.00003907469,0.00008642139,0.000649875],"genre_scores_gemma":[0.3654526,0.0002577636,0.6304782,0.0003263687,0.00006263596,0.00218275,0.0001467696,0.00007629331,0.001016446],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04678274,"threshold_uncertainty_score":0.2474138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.26585290794277,"score_gpt":0.4335272839070256,"score_spread":0.1676743759642556,"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."}}