{"id":"W2084976008","doi":"10.1002/cjs.11174","title":"D‐optimal minimax fractional factorial designs","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimax; Fractional factorial design; Mathematics; Factorial experiment; Plackett–Burman design; Factorial; Invariant (physics); Optimal design; Mathematical optimization; Applied mathematics; Statistics; Response surface methodology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.02288725,0.001387254,0.003150031,0.002423986,0.001026057,0.001800767,0.001640005,0.002011973,0.007492699],"category_scores_gemma":[0.05622072,0.0007856262,0.001511126,0.001537752,0.00239997,0.001845174,0.00223016,0.001887247,0.000715575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001736707,"about_ca_system_score_gemma":0.00219423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005211117,"about_ca_topic_score_gemma":0.0003516519,"domain_scores_codex":[0.9779954,0.01667456,0.000799252,0.001714386,0.00226163,0.0005547684],"domain_scores_gemma":[0.9649829,0.02830322,0.001640062,0.002170612,0.002452464,0.0004507546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003041666,0.0004892711,0.003032866,0.001193729,0.0003091379,0.0001836041,0.0004756527,0.1958125,0.01670546,0.4214,0.003277404,0.3540788],"study_design_scores_gemma":[0.0007105874,0.002463054,0.00283804,0.0003143736,0.0001196016,0.0001706519,0.0001200342,0.5737791,0.0155552,0.3908013,0.01297775,0.0001502708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01232765,0.0002005587,0.9851192,0.0001297891,0.00003324639,0.0002689676,0.00007758236,0.00009996651,0.001742992],"genre_scores_gemma":[0.2081886,0.000198855,0.7870282,0.0002520801,0.00004736725,0.00213434,0.0001785325,0.0001075594,0.001864395],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02288725,"threshold_uncertainty_score":0.1210408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1962650104295456,"score_gpt":0.4038326614090677,"score_spread":0.2075676509795221,"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."}}