{"id":"W2017226599","doi":"10.1093/biomet/asn064","title":"Construction of orthogonal and nearly orthogonal Latin hypercubes","year":2009,"lang":"en","type":"article","venue":"Biometrika","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Hypercube; Mathematics; Latin hypercube sampling; Library science; Combinatorics; Statistics; Computer science","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":[0.0001438566,0.0001152604,0.0001681493,0.0006024466,0.00007624653,0.0000526237,0.0002034466,0.00006259458,0.00001883369],"category_scores_gemma":[0.000139608,0.000110422,0.00003987747,0.002048233,0.0001248724,0.0004606788,0.00006667635,0.00007465718,0.000007084783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002465855,"about_ca_system_score_gemma":0.00005324788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003354696,"about_ca_topic_score_gemma":7.246634e-7,"domain_scores_codex":[0.9989963,0.00003468676,0.0002346623,0.0003076088,0.0002615952,0.0001651719],"domain_scores_gemma":[0.9992916,0.00008796874,0.0001249698,0.0002168446,0.0001933823,0.00008522723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002215905,0.0001400188,0.01171421,0.00001009247,0.00002466744,0.000009464619,0.0002423973,0.0003594007,0.01155115,0.07763699,0.00003846665,0.898251],"study_design_scores_gemma":[0.002927095,0.001077397,0.7891611,0.00005653569,0.00002498854,0.0003078147,0.0001366251,0.1594781,0.02517643,0.01762043,0.003170704,0.0008627482],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1298251,0.0002899448,0.868283,0.00026226,0.0002029154,0.0001299885,0.00001292212,0.0001006177,0.0008931896],"genre_scores_gemma":[0.431949,0.00004371748,0.5678203,0.00007187229,0.00003465069,0.000001622031,0.000004026294,0.00000432584,0.00007048308],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8973882,"threshold_uncertainty_score":0.450288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01157170578851459,"score_gpt":0.2410396318267071,"score_spread":0.2294679260381925,"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."}}