{"id":"W3146408440","doi":"10.1080/03610918.2022.2057537","title":"A MCMC-type simple probabilistic approach for determining optimal progressive censoring schemes","year":2022,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Censoring (clinical trials); Probabilistic logic; Mathematical optimization; Simple (philosophy); Computer science; Set (abstract data type); Algorithm; Cardinality (data modeling); Scheme (mathematics); Markov chain Monte Carlo; Mathematics; Bayesian probability; Data mining; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004479413,0.0001676783,0.0002317454,0.0001665146,0.0009458176,0.00009362341,0.0002848793,0.00004713967,0.00004942003],"category_scores_gemma":[0.001536774,0.0002012364,0.00003106228,0.000504903,0.0001619654,0.0001144571,0.0002615231,0.0002349822,0.000001775996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000177456,"about_ca_system_score_gemma":0.0000849311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004266705,"about_ca_topic_score_gemma":0.000002591042,"domain_scores_codex":[0.9983452,0.000230013,0.0006359211,0.0003185194,0.0002486714,0.0002216711],"domain_scores_gemma":[0.9957021,0.003095459,0.0003082443,0.000441726,0.0003769314,0.00007546999],"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":[0.00003921847,0.0003409364,0.0002358713,0.00009340612,0.00001483565,4.296463e-7,0.0006354452,0.3638849,0.0000148659,0.6166794,0.000249499,0.01781121],"study_design_scores_gemma":[0.0006959874,0.00008066903,0.001171066,0.0000105816,0.00003696073,0.000005171417,0.0005738809,0.8643852,0.00000290707,0.1314443,0.001406831,0.0001864845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01216346,0.00007792219,0.9853131,0.0001102997,0.00003860733,0.001290232,0.0006720541,0.0001111912,0.0002231663],"genre_scores_gemma":[0.5647174,0.000003479075,0.43281,0.0000375099,0.000009890792,0.0005920631,0.001791843,0.00001706142,0.00002074858],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5525539,"threshold_uncertainty_score":0.8206182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3032473239562707,"score_gpt":0.4998736621786009,"score_spread":0.1966263382223302,"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."}}