{"id":"W4382699702","doi":"10.1007/s10958-023-06534-7","title":"Estimating the Amount of Sparsity in Two-Point Mixture Models","year":2023,"lang":"en","type":"article","venue":"Journal of Mathematical Sciences","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Estimator; Minimax; Variable (mathematics); Selection (genetic algorithm); Applied mathematics; Fraction (chemistry); Model selection; Focus (optics); Minimax estimator; Mathematical optimization; Statistics; Minimum-variance unbiased estimator; Mathematical analysis; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01013493,0.0009839475,0.002525701,0.002013206,0.0008972252,0.002967879,0.002943395,0.003653937,0.001455793],"category_scores_gemma":[0.07676541,0.002343615,0.001490453,0.001650815,0.002933519,0.005833724,0.003871182,0.004384821,0.0004440378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009308153,"about_ca_system_score_gemma":0.00105174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003567086,"about_ca_topic_score_gemma":0.003601742,"domain_scores_codex":[0.9961764,0.002185051,0.0001938565,0.0006044059,0.0006230532,0.0002172187],"domain_scores_gemma":[0.9221084,0.07147193,0.001680165,0.002497429,0.001717299,0.0005246899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001199211,0.0003161651,0.0124496,0.0007565125,0.0006267255,0.0003113608,0.0007147499,0.6878179,0.009028382,0.1602873,0.003549742,0.1229424],"study_design_scores_gemma":[0.00002822217,0.00002859002,0.0008183933,0.00003297798,0.00003507981,0.00006088633,0.00003306177,0.93159,0.0007234898,0.06628502,0.0003356166,0.00002862377],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03718381,0.0003150984,0.9611261,0.0006077186,0.00003140231,0.00001990703,0.0000983288,0.0001230716,0.0004945301],"genre_scores_gemma":[0.718223,0.00098524,0.2761642,0.0004732055,0.0003605271,0.0001824653,0.001026143,0.0002377674,0.00234744],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01013493,"threshold_uncertainty_score":0.0535993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05897563886771148,"score_gpt":0.3426116625892489,"score_spread":0.2836360237215374,"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."}}