{"id":"W2135892144","doi":"10.18637/jss.v033.i06","title":"Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering with<b>tgp</b>Version 2, an<i>R</i>Package for Treed Gaussian Process Models","year":2010,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"ca_institutions":"Booth University College","funders":"Engineering and Physical Sciences Research Council","keywords":"Categorical variable; Computer science; Gaussian process; Covariate; Markov chain Monte Carlo; Sensitivity (control systems); Bayesian probability; Algorithm; Gaussian; Artificial intelligence; Machine learning; Engineering","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":[0.0004294189,0.0001948471,0.000418677,0.0001784014,0.0001978423,0.0002948277,0.0003073695,0.0001091443,0.0000143776],"category_scores_gemma":[0.0002455896,0.0001419481,0.00006179349,0.0005640762,0.0001110492,0.001472471,0.00005523206,0.0003551253,4.126398e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003114942,"about_ca_system_score_gemma":0.0002005246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001022565,"about_ca_topic_score_gemma":0.00006095151,"domain_scores_codex":[0.9984395,0.00004504651,0.0004409241,0.0003615305,0.0004214653,0.000291563],"domain_scores_gemma":[0.9981918,0.0002699805,0.0003898047,0.0002480393,0.0005373741,0.0003629555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002171859,0.002901386,0.2250522,0.002404335,0.001870961,0.003260718,0.007021142,0.3614329,0.00638457,0.276996,0.001098059,0.1094059],"study_design_scores_gemma":[0.001055809,0.0007429998,0.01727372,0.00004577588,0.000309659,0.0004700559,0.00007340455,0.9522681,0.0005898789,0.02672263,0.00002830802,0.0004196658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0291948,0.00002275843,0.9701558,0.0003313259,0.00007329058,0.0001219926,0.00004452387,0.00004196188,0.00001357123],"genre_scores_gemma":[0.5772945,0.000008567083,0.4225658,0.00006126772,0.00004465449,0.000002699632,0.00001071471,0.000008285996,0.000003563798],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5908352,"threshold_uncertainty_score":0.5788474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00885045248584295,"score_gpt":0.251482053128426,"score_spread":0.242631600642583,"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."}}