{"id":"W2053652664","doi":"10.1080/10618600.2013.841584","title":"Parallel Bayesian Additive Regression Trees","year":2014,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"Office of Science; U.S. Department of Energy","keywords":"Markov chain Monte Carlo; Computer science; Bayesian probability; Boosting (machine learning); Approximate Bayesian computation; Bayesian inference; Inference; Computation; Machine learning; Algorithm; Artificial intelligence","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":[0.0002186912,0.0001099097,0.000203586,0.0001227798,0.000123236,0.000133729,0.0002752533,0.00004696703,0.00001434145],"category_scores_gemma":[0.0001401353,0.00007571774,0.00004724539,0.0002057882,0.0001041129,0.000250544,0.0000558119,0.000190709,0.00000225358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007893298,"about_ca_system_score_gemma":0.00007269059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001666933,"about_ca_topic_score_gemma":0.000002214156,"domain_scores_codex":[0.9988847,0.00007508211,0.0003696555,0.0001375593,0.0003974826,0.0001355416],"domain_scores_gemma":[0.998534,0.0005623002,0.0003106119,0.00007058605,0.0003434714,0.0001790294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002551592,0.00005464186,0.0005793546,0.00001400488,0.00001975851,0.00002632467,0.0001010541,0.0008142871,0.000003347766,0.8625697,0.00253023,0.1332617],"study_design_scores_gemma":[0.0003711856,0.000306753,0.05063203,0.0000519885,0.000008859745,0.000142721,0.000006699973,0.1411783,0.000005108753,0.805434,0.001766793,0.0000955595],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001181286,0.0000841874,0.9966915,0.001683465,0.0001164692,0.00002591214,0.00001735504,0.00001063045,0.0001892535],"genre_scores_gemma":[0.5968732,0.0000485135,0.402731,0.0002585811,0.00007125547,5.146971e-7,0.000003545736,0.000002844259,0.00001056192],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5956919,"threshold_uncertainty_score":0.308768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007996393648830565,"score_gpt":0.2454976015265561,"score_spread":0.2375012078777256,"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."}}