{"id":"W3205092057","doi":"10.1002/cjs.11651","title":"Testing homogeneity in contaminated mixture models","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Homogeneity (statistics); Limiting; Computer science; Null hypothesis; Mathematics; Biological system; Statistics; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0006537105,0.0001051345,0.0002607331,0.0001876943,0.00007137674,0.0001398233,0.000435818,0.0000729377,0.00001789134],"category_scores_gemma":[0.0007216405,0.000101776,0.00003725147,0.0005147765,0.00003714068,0.0002460906,0.00002439647,0.0003221645,0.000001719507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001250288,"about_ca_system_score_gemma":0.002804718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001089341,"about_ca_topic_score_gemma":0.01700743,"domain_scores_codex":[0.9987339,0.0001977201,0.0004488807,0.0001558198,0.0001661159,0.0002975074],"domain_scores_gemma":[0.998081,0.0002482469,0.0001973864,0.0002389458,0.0007596461,0.0004747231],"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":[0.000003130574,0.00003347941,0.004321462,0.00003695027,0.00003723847,0.01607382,0.001718181,0.001400354,0.0009553997,0.5917931,0.008071958,0.375555],"study_design_scores_gemma":[0.0009581913,0.0001475081,0.02454453,0.0002362477,0.00003033652,0.002409643,0.00007822788,0.2153612,0.001446792,0.7513493,0.002988004,0.0004499894],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004161878,0.0006763433,0.9929847,0.0003677194,0.0003836865,0.0000330097,0.00006945846,0.000003619336,0.001319638],"genre_scores_gemma":[0.2895019,0.000007808576,0.7100457,0.0003170108,0.00004030685,3.276479e-7,0.000001867146,0.000006304783,0.00007880054],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.375105,"threshold_uncertainty_score":0.9490548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0360547725776767,"score_gpt":0.2493521132847954,"score_spread":0.2132973407071187,"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."}}