{"id":"W2111666498","doi":"10.1093/biomet/ass065","title":"Strong orthogonal arrays and associated Latin hypercubes for computer experiments","year":2012,"lang":"en","type":"article","venue":"Biometrika","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Latin hypercube sampling; Hypercube; Beijing; China; Mathematics; Library science; Combinatorics; Statistics; Computer science; Geography; Monte Carlo method; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001651798,0.0001208257,0.0002211936,0.000435476,0.0001113541,0.0001059243,0.0002097673,0.00008823171,0.00007962451],"category_scores_gemma":[0.001663654,0.00008690901,0.00006474629,0.0009056764,0.00005783574,0.0001879752,0.00007484145,0.00005053823,0.0000422331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003818472,"about_ca_system_score_gemma":0.00001948047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002712308,"about_ca_topic_score_gemma":3.408158e-7,"domain_scores_codex":[0.9985787,0.00005151368,0.0003069827,0.0002417674,0.0004877771,0.0003332108],"domain_scores_gemma":[0.9982087,0.001240108,0.00008388357,0.0001871398,0.0001164894,0.0001636933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000203294,0.00189831,0.492851,0.00006796463,0.0005701424,0.000006532208,0.003800892,0.002173537,0.02446427,0.06897354,0.1145775,0.290413],"study_design_scores_gemma":[0.00449568,0.0009060475,0.776822,0.0000849391,0.0001084174,0.0000205244,0.0007972106,0.1076556,0.00561562,0.008132859,0.09381425,0.001546804],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3473894,0.0007375857,0.6504215,0.00004722437,0.0008077181,0.0002173357,0.00004899523,0.00005620207,0.0002740372],"genre_scores_gemma":[0.9538346,0.000003584272,0.04539087,0.0000463769,0.0002448352,0.0000166809,0.00001050539,0.00001138713,0.0004411698],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6064452,"threshold_uncertainty_score":0.3544047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2075949699799988,"score_gpt":0.3704516957843537,"score_spread":0.162856725804355,"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."}}