{"id":"W3205724065","doi":"10.48550/arxiv.1612.01619","title":"mBART: Multidimensional Monotone BART","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Monotonic function; Monotone polygon; Markov chain Monte Carlo; Parametric statistics; Computer science; Mathematics; Set (abstract data type); Nonparametric statistics; Bayesian probability; Multivariate statistics; Mathematical optimization; Algorithm; Applied mathematics; Econometrics; Machine learning; Artificial intelligence; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006837305,0.001308155,0.001622829,0.001341428,0.0008261647,0.002284471,0.003323058,0.001853867,0.008400226],"category_scores_gemma":[0.03985556,0.00120074,0.001540967,0.001931574,0.001834273,0.003610702,0.004066156,0.00398589,0.003853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008263954,"about_ca_system_score_gemma":0.001937262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002753665,"about_ca_topic_score_gemma":0.003362141,"domain_scores_codex":[0.9960306,0.002189348,0.000190073,0.000451619,0.0009740227,0.0001642897],"domain_scores_gemma":[0.9836662,0.01080785,0.001222428,0.00262542,0.001228865,0.0004491128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005013545,0.0001732555,0.003569615,0.0004538168,0.0002441063,0.0004245719,0.0003511419,0.1525569,0.002987591,0.56871,0.02760878,0.2424189],"study_design_scores_gemma":[0.00004155107,0.00007772747,0.0003864542,0.00007634296,0.00002881173,0.000208545,0.00001746167,0.7567421,0.001019633,0.2293092,0.01205054,0.00004170422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00227354,0.0004028962,0.9933571,0.0004051279,0.00007389151,0.00006270475,0.0002952536,0.001414927,0.001714536],"genre_scores_gemma":[0.1445355,0.001382236,0.8429101,0.001087694,0.0004627987,0.0008192795,0.001521927,0.001309364,0.005971138],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008400226,"threshold_uncertainty_score":0.03615957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2023274158177658,"score_gpt":0.2700564577696237,"score_spread":0.06772904195185789,"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."}}