{"id":"W2006148284","doi":"10.1002/cjs.11147","title":"A cluster‐sample approach for Monte Carlo integration using multiple samplers","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Estimator; Monte Carlo method; Sample (material); Statistics; Sample size determination; Cluster (spacecraft); Variance reduction; Variance (accounting); Gaussian; Monte Carlo integration; Mathematics; Computer science; Field (mathematics); Markov chain Monte Carlo; Monte Carlo molecular modeling; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0008322265,0.0001466788,0.0003188628,0.0001633454,0.0001595429,0.00006329613,0.0001524141,0.00007887989,0.00005677401],"category_scores_gemma":[0.01592794,0.0001278664,0.00006925087,0.0001068538,0.00009257001,0.0001355127,0.000008101843,0.0001997824,6.626141e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002350544,"about_ca_system_score_gemma":0.0004961937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004335144,"about_ca_topic_score_gemma":0.004226078,"domain_scores_codex":[0.9986526,0.0001058996,0.0005388088,0.00009074899,0.0001654607,0.0004465075],"domain_scores_gemma":[0.9949989,0.003332138,0.0003417782,0.0001325506,0.0004890559,0.0007056335],"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":[0.0001688052,0.0002026154,0.02339101,0.0005858096,0.0002979083,0.00002004334,0.008953519,0.00129984,0.0003545265,0.7937418,0.04560376,0.1253804],"study_design_scores_gemma":[0.002194437,0.0005204058,0.003137981,0.0002328323,0.0005851277,0.0002436464,0.003711806,0.671737,0.0002510071,0.3089824,0.007618153,0.0007851818],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009082069,0.00008976543,0.9877374,0.00002689033,0.0004674698,0.0002304662,0.002274085,0.000004331503,0.00008756829],"genre_scores_gemma":[0.313035,0.00000232089,0.6866338,0.00006685233,0.0002119875,0.000003964779,0.000009914124,0.00002108812,0.00001501084],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6704372,"threshold_uncertainty_score":0.9923613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2503426805147262,"score_gpt":0.3606109690667924,"score_spread":0.1102682885520662,"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."}}