{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008715302,0.0001878476,0.0004404569,0.0006899647,0.0003152495,0.0003829839,0.001045224,0.000104238,0.0009508726],"category_scores_gemma":[0.009002369,0.0001522531,0.0001404396,0.0004535658,0.0005612501,0.0004466449,0.00002852312,0.0001856966,0.0001050765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002647682,"about_ca_system_score_gemma":0.003699715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002529281,"about_ca_topic_score_gemma":0.0006968905,"domain_scores_codex":[0.9964601,0.0007203264,0.001196777,0.0002699872,0.0008516392,0.0005011159],"domain_scores_gemma":[0.9915169,0.003981402,0.0005935942,0.0003758117,0.002309934,0.001222336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008055554,0.0001241819,0.0006176443,0.00001530729,0.0001862356,0.001935945,0.007022961,0.03340601,0.02655417,0.06380051,0.747264,0.1110175],"study_design_scores_gemma":[0.004046489,0.01536237,0.005762374,0.0001277517,0.0001423218,0.001149814,0.004578111,0.4900701,0.01047816,0.3525003,0.1146135,0.001168686],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02761484,0.0001796567,0.9694266,0.000339789,0.0009814417,0.0002029346,0.0006265689,0.000003893034,0.0006243142],"genre_scores_gemma":[0.3493755,0.000001994804,0.6492069,0.0003457466,0.0001976059,0.00000262025,0.000001695175,0.00002239463,0.0008454514],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6326504,"threshold_uncertainty_score":0.9999624,"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."}}