{"id":"W1977500253","doi":"10.1920/wp.cem.2014.1114","title":"Nonparametric estimation of finite mixtures","year":2013,"lang":"en","type":"preprint","venue":"Econstor (Econstor)","topic":"Italy: Economic History and Contemporary Issues","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Core Research for Evolutional Science and Technology; Economic and Social Research Council; Université de Montréal; Vanderbilt University","keywords":"Nonparametric statistics; Estimation; Econometrics; Applied mathematics; Mathematics; Computer science; Statistics; Economics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00103447,0.000816602,0.002205067,0.001742057,0.0001999752,0.000182368,0.001105368,0.0009878839,0.006873237],"category_scores_gemma":[0.0006026489,0.001066238,0.0008205231,0.0003391902,0.0005512714,0.0006944519,0.0004543839,0.001009206,0.006414888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005989871,"about_ca_system_score_gemma":0.0003466971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008401268,"about_ca_topic_score_gemma":0.00003349689,"domain_scores_codex":[0.9948289,0.00008735747,0.002809217,0.001543057,0.00009374943,0.0006377104],"domain_scores_gemma":[0.9942708,0.0004489338,0.003150535,0.001727688,0.0001236934,0.000278352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001160475,0.0008854851,0.7442155,0.001612289,0.001605561,0.00004255072,0.003857756,0.01511827,0.0000380561,0.1637456,0.06439355,0.004369304],"study_design_scores_gemma":[0.007109803,0.001119202,0.1419804,0.001804134,0.000513987,0.000123867,0.0006972842,0.1280293,0.002950274,0.3529997,0.3507729,0.01189901],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8404831,0.01969657,0.002004382,0.0002210791,0.005773613,0.0009408243,0.001136664,0.0001705279,0.1295733],"genre_scores_gemma":[0.9866538,0.0003658014,0.003226767,0.0002503359,0.0004194853,0.0002484517,0.0003001861,0.000114345,0.008420801],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6022351,"threshold_uncertainty_score":0.9991788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02546381750566702,"score_gpt":0.2210088947864274,"score_spread":0.1955450772807604,"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."}}