{"id":"W3086585527","doi":"10.1017/s0269964820000467","title":"ORDERINGS OF FINITE MIXTURE MODELS WITH LOCATION-SCALE DISTRIBUTED COMPONENTS","year":2020,"lang":"en","type":"article","venue":"Probability in the Engineering and Informational Sciences","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Scale (ratio); Mixture model; Mixing (physics); Majorization; Stochastic ordering; Applied mathematics; Mathematics; Hazard; Computer science; Statistical physics; Statistics; Combinatorics; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004666965,0.001095934,0.001120807,0.001564333,0.0009699155,0.002261609,0.002367021,0.001354304,0.005055878],"category_scores_gemma":[0.01642279,0.0008596756,0.001520873,0.001442588,0.001929393,0.003576317,0.001495509,0.002223381,0.0008650743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001822682,"about_ca_system_score_gemma":0.001241498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004327251,"about_ca_topic_score_gemma":0.005342615,"domain_scores_codex":[0.9973752,0.001197019,0.0001465342,0.0004826294,0.0005596771,0.000238971],"domain_scores_gemma":[0.9909807,0.005969354,0.0009942445,0.0008151012,0.0008097783,0.0004308652],"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":[0.00008698663,0.00004369135,0.002042019,0.00007907092,0.00004337892,0.0002737753,0.0002704943,0.05526614,0.001466849,0.925104,0.0009920533,0.01433151],"study_design_scores_gemma":[0.00002621335,0.00004020632,0.0007539816,0.00002451419,0.0000326185,0.0001551944,0.0000528178,0.3950128,0.000713035,0.600601,0.002548495,0.00003908564],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02901501,0.0003800862,0.966036,0.0004601628,0.00005944902,0.00004975591,0.0002172863,0.0001659983,0.00361623],"genre_scores_gemma":[0.6813463,0.001614233,0.2950426,0.0006077865,0.0004323499,0.0004564535,0.001794601,0.0003672708,0.0183384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005055878,"threshold_uncertainty_score":0.02468157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06509825568933962,"score_gpt":0.2734113843583102,"score_spread":0.2083131286689706,"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."}}