{"id":"W1587669494","doi":"10.1002/cjs.5550360305","title":"Marginally restricted sequential D‐optimal designs","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"A priori and a posteriori; Mathematical optimization; Mathematics; Set (abstract data type); Variable (mathematics); Optimal design; Value (mathematics); Sequential analysis; Computer science; Algorithm; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.02928991,0.001723909,0.003090481,0.00137432,0.0007160048,0.001336636,0.002191145,0.002182425,0.008720176],"category_scores_gemma":[0.07153775,0.001663858,0.001665039,0.001113696,0.00353838,0.001926818,0.003226473,0.00250818,0.001073769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001648581,"about_ca_system_score_gemma":0.002454678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007300402,"about_ca_topic_score_gemma":0.0005212093,"domain_scores_codex":[0.9415861,0.05097737,0.001700579,0.002964409,0.002099049,0.0006725398],"domain_scores_gemma":[0.9197173,0.06401388,0.004292602,0.007373036,0.003470225,0.001133033],"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":[0.01216441,0.001421655,0.003797943,0.002333376,0.0007716285,0.0002276872,0.0008409857,0.2173552,0.009756903,0.4948495,0.002504486,0.2539762],"study_design_scores_gemma":[0.002993206,0.00511539,0.001679305,0.0003297167,0.0002248396,0.0001399747,0.0001272055,0.4644787,0.006932418,0.5077035,0.01012018,0.0001556471],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01390034,0.0002399797,0.9829645,0.0001969679,0.00006958225,0.0006744273,0.00007976375,0.0001544739,0.00171987],"genre_scores_gemma":[0.2563601,0.0002721428,0.7369806,0.000389123,0.00007575937,0.004061079,0.0001765959,0.00006955771,0.001615178],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02928991,"threshold_uncertainty_score":0.1549017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2719362671563821,"score_gpt":0.4007528114557431,"score_spread":0.128816544299361,"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."}}