{"id":"W4409109035","doi":"10.5539/ijsp.v14n1p50","title":"Algorithmic Construction of Bayesian Optimal Block Designs Using the Linear Mixed Effects Model","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mathematics; Bayesian probability; Block (permutation group theory); Mathematical optimization; Mixed model; Linear model; Applied mathematics; Computer science; Algorithm; Statistics; Combinatorics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003170237,0.0001029482,0.0002651919,0.0001754118,0.00007783849,0.0001054787,0.0004856069,0.00005080315,0.00001742946],"category_scores_gemma":[0.002900116,0.00006644249,0.0000814059,0.0001851972,0.0003726023,0.0001598831,0.0001165988,0.0001758542,3.551491e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008472236,"about_ca_system_score_gemma":0.0002274745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001677847,"about_ca_topic_score_gemma":0.000001500717,"domain_scores_codex":[0.9975621,0.000427881,0.0008787079,0.0001727895,0.0008616956,0.00009680857],"domain_scores_gemma":[0.9954541,0.002282437,0.0005858323,0.0001579934,0.001463821,0.00005586634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001820744,0.0006811321,0.01600467,0.0001136399,0.0007712146,0.0000589093,0.00180856,0.3198424,0.07730213,0.2444988,0.001038564,0.3360592],"study_design_scores_gemma":[0.0004308331,0.000118784,0.001552209,0.00004836632,0.00003661321,0.00007165318,0.0001458068,0.6988355,0.01271704,0.2859478,0.00003746615,0.00005792807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.17174,0.0001081122,0.8270245,0.0001769588,0.0006607265,0.0001363684,0.00006925406,0.000002042355,0.00008204139],"genre_scores_gemma":[0.4100223,0.0000116189,0.5898868,0.00002818359,0.00003191952,9.55975e-7,4.575659e-7,0.000002539495,0.00001526661],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.378993,"threshold_uncertainty_score":0.347192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08904698293725741,"score_gpt":0.4315362966326311,"score_spread":0.3424893136953737,"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."}}