{"id":"W2163496953","doi":"10.1080/01441640902829454","title":"Numerical Analysis of the Statistical Properties of Uniform Design in Stated Choice Modelling","year":2009,"lang":"en","type":"article","venue":"Transport Reviews","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Orthogonality; Estimator; Design of experiments; Variance (accounting); Orthogonal array; Fractional factorial design; Mathematical optimization; Monte Carlo method; Computer science; Optimal design; Econometrics; Scale (ratio); Covariance matrix; Measure (data warehouse); Covariance; Taguchi methods; Factorial; Mathematics; Factorial experiment; Statistics; Algorithm; Economics; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006501687,0.00007973813,0.0005723538,0.00009878248,0.0000149004,0.000002089159,0.0001174127,0.00003364902,0.0001855642],"category_scores_gemma":[0.00001380606,0.00006157568,0.0001354492,0.0003535945,0.00003882696,0.00007889293,0.000002411217,0.00006332305,0.00001382234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004914433,"about_ca_system_score_gemma":0.000008139105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000393554,"about_ca_topic_score_gemma":0.00003188019,"domain_scores_codex":[0.9987295,0.00002672788,0.0009606567,0.0001553257,0.00002570717,0.0001020286],"domain_scores_gemma":[0.9994631,0.00001728454,0.0003070409,0.0001868616,0.00000445447,0.00002128998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00002186892,0.0001821644,0.466128,0.00007977328,0.00009358591,2.327791e-7,0.0006898317,0.5215347,0.00007156188,0.008468922,0.00001082644,0.002718586],"study_design_scores_gemma":[0.0002235849,0.00004648043,0.7716421,0.00005297067,0.0001225416,1.090906e-7,0.00001282281,0.2250609,0.0002219765,0.001367652,0.001129475,0.0001193811],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6944661,0.005042823,0.2992005,0.00007958937,0.00002324945,0.0004386051,0.00004988458,0.00000382811,0.0006954673],"genre_scores_gemma":[0.9945363,0.002603233,0.002732119,0.00005277657,0.000003271239,0.000008150063,0.00001670378,0.000004277269,0.000043208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3055142,"threshold_uncertainty_score":0.2510983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2219103099088347,"score_gpt":0.251536241719039,"score_spread":0.02962593181020429,"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."}}