{"id":"W2940123489","doi":"10.1002/cjs.11494","title":"Design selection for strong orthogonal arrays","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Selection (genetic algorithm); Class (philosophy); Space (punctuation); Computer science; Focus (optics); Design of experiments; Theoretical computer science; Artificial intelligence; Mathematics; Statistics; Physics; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003156291,0.0001093379,0.0002735602,0.0003954927,0.0001238575,0.0002266766,0.0003288963,0.00006063147,0.001331247],"category_scores_gemma":[0.002350655,0.00009187084,0.00007505506,0.0003052089,0.00006516215,0.000255325,0.000006435032,0.0001668673,0.00009265608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002068757,"about_ca_system_score_gemma":0.001914062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001656538,"about_ca_topic_score_gemma":0.00090536,"domain_scores_codex":[0.9980751,0.0002443494,0.000642391,0.000166914,0.0005638133,0.0003074904],"domain_scores_gemma":[0.9960983,0.001862997,0.0004325504,0.0001362328,0.0009289227,0.0005410254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007864325,0.00008531461,0.05194485,0.00003816387,0.0002590315,0.0002168486,0.002562433,0.09176095,0.03221041,0.3037687,0.3501167,0.1662502],"study_design_scores_gemma":[0.004841354,0.007695726,0.01888482,0.0001428074,0.0001798362,0.001386896,0.004332981,0.2436445,0.01941065,0.5743596,0.1237629,0.001357997],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007841905,0.00008612248,0.9898916,0.00007104285,0.001064647,0.0002197367,0.0001477857,0.000002423232,0.0006747442],"genre_scores_gemma":[0.2444739,0.00000156797,0.7543864,0.0001114888,0.0001179075,0.000001931359,0.000002211007,0.00001498424,0.0008895716],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2705908,"threshold_uncertainty_score":0.9995817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1751092719048859,"score_gpt":0.4016140556802749,"score_spread":0.226504783775389,"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."}}