{"id":"W2083839279","doi":"10.1198/004017007000000038","title":"Incorporating Prior Information in Optimal Design for Model Selection","year":2007,"lang":"en","type":"article","venue":"Technometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; National Science Foundation","keywords":"Interpretability; Bayesian information criterion; Set (abstract data type); Selection (genetic algorithm); Computer science; Prior probability; Bayesian probability; Model selection; Hellinger distance; Machine learning; Identification (biology); Design of experiments; Mathematics; Mathematical optimization; Artificial intelligence; Data mining; Statistics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01595649,0.0001420012,0.0002317745,0.004548046,0.0001356565,0.0002076095,0.0005131824,0.0001858552,0.00001124891],"category_scores_gemma":[0.01267918,0.0001281245,0.00006626178,0.01074741,0.00004845509,0.001443481,0.0001001916,0.0001846526,0.00003897474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000331651,"about_ca_system_score_gemma":0.00009877978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007816485,"about_ca_topic_score_gemma":0.000002091612,"domain_scores_codex":[0.9974188,0.00005416389,0.0009785307,0.0002829124,0.000929598,0.0003359606],"domain_scores_gemma":[0.9969063,0.001991142,0.0003985731,0.0002622111,0.0003633773,0.00007838851],"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":[0.0002683284,0.0001344327,0.002499468,0.00000985681,0.000004510731,0.000001221023,0.0004419253,0.3485863,0.01695369,0.01220232,0.0006528465,0.6182451],"study_design_scores_gemma":[0.0005073551,0.0002201285,0.0006106996,0.000004931433,0.000002820283,0.000003616632,0.0004570906,0.9215321,0.05575415,0.020386,0.0003505315,0.0001705463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06615272,0.00004207562,0.9316128,0.00003131581,0.00008998001,0.0007781978,0.000004616456,0.0001313948,0.001156883],"genre_scores_gemma":[0.4386746,0.000001070566,0.5611628,0.0000428595,0.00001174368,0.00003185014,0.000001867193,0.000007352762,0.00006586518],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6180746,"threshold_uncertainty_score":0.9956375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.214121415020881,"score_gpt":0.4495098550792087,"score_spread":0.2353884400583277,"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."}}