{"id":"W2161202741","doi":"10.5555/1036843.1036873","title":"From fields to trees","year":2004,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gibbs sampling; Markov chain Monte Carlo; Graphical model; Tree (set theory); Sampling (signal processing); Computer science; Focus (optics); Markov chain; Importance sampling; Posterior probability; Algorithm; Mathematics; Belief propagation; Statistics; Artificial intelligence; Bayesian probability; Monte Carlo method; Machine learning; Combinatorics","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":[0.0003711313,0.000169811,0.0002111195,0.0001676101,0.00007009054,0.0001490622,0.001040973,0.0001130507,0.00005402944],"category_scores_gemma":[0.0001811629,0.0001578594,0.00007128077,0.0007039856,0.00004564588,0.0002187374,0.0001885216,0.0002235041,0.000271456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001064953,"about_ca_system_score_gemma":0.00009454368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00386142,"about_ca_topic_score_gemma":0.004089796,"domain_scores_codex":[0.9983639,0.00007507892,0.0004008053,0.000549086,0.000228434,0.0003827247],"domain_scores_gemma":[0.9989518,0.0001666429,0.00004645329,0.0006154267,0.0000581011,0.0001616033],"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":[0.00001413436,0.00007165864,0.000009125511,0.00000127059,0.000003570915,0.00002990977,0.004499635,0.04243318,0.00106382,0.4923437,0.0000597021,0.4594703],"study_design_scores_gemma":[0.00003134611,0.00008257264,0.00006229851,0.00004536811,0.000001837327,0.000001910433,0.0001271646,0.03047827,0.03197251,0.9363816,0.0005910659,0.0002240598],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02321407,0.00005140634,0.9681598,0.006199467,0.0006125178,0.0001895092,0.000004203862,0.00009956339,0.001469438],"genre_scores_gemma":[0.6835588,0.000007744668,0.3148934,0.001315478,0.000155495,0.00001905499,0.000001439722,0.000005988549,0.00004262269],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6603447,"threshold_uncertainty_score":0.6437319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04739542792282484,"score_gpt":0.3219355585509258,"score_spread":0.274540130628101,"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."}}