{"id":"W2280270471","doi":"10.1007/s11222-016-9641-6","title":"Mixture models: building a parameter space","year":2016,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Space (punctuation); Parameter space; Mixture model; Mathematics; Computer science; Applied mathematics; 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":[0.0003262143,0.0001427568,0.0001703337,0.00005268644,0.0001462153,0.0001566493,0.0002847353,0.00005292424,0.000002756527],"category_scores_gemma":[0.00007468563,0.00009541875,0.00002663751,0.0001093241,0.00004525104,0.0001752119,0.00027212,0.00009226635,0.000002698441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001482208,"about_ca_system_score_gemma":0.0000269994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009354204,"about_ca_topic_score_gemma":0.00000122973,"domain_scores_codex":[0.9989085,0.00007819918,0.0001764097,0.0003838394,0.0001469595,0.0003061348],"domain_scores_gemma":[0.9989184,0.0005389689,0.00008061268,0.0002818078,0.00006370754,0.0001164598],"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":[8.604093e-7,0.000004463673,0.00001367189,0.000006514449,0.000005612847,0.00001057944,0.0001520301,0.00001089719,0.0003687446,0.5894294,0.0004186189,0.4095786],"study_design_scores_gemma":[0.000159492,0.00002807239,0.00005880297,0.00005087987,0.000004483707,0.00002586181,0.000002491663,0.425355,0.0002230875,0.5733413,0.0006103512,0.0001401759],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001782538,0.000224884,0.9962482,0.0006684301,0.0002208386,0.00006923927,0.00001397334,0.00008304266,0.0006888727],"genre_scores_gemma":[0.2995619,0.00002646626,0.7000446,0.0001947757,0.00005608345,0.000001011242,2.898235e-7,0.000007751354,0.0001071414],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4253441,"threshold_uncertainty_score":0.3891064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01936795463846525,"score_gpt":0.2714557642212007,"score_spread":0.2520878095827354,"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."}}