{"id":"W2112335378","doi":"10.1002/cjs.10083","title":"New estimation and feature selection methods in mixture‐of‐experts models","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feature selection; Estimator; Feature (linguistics); Selection (genetic algorithm); Model selection; Maximum likelihood; Mathematics; Statistics; Computer science; Likelihood function; Function (biology); Restricted maximum likelihood; Pattern recognition (psychology); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008430522,0.0001022569,0.0003004855,0.0002255195,0.00003980878,0.0000383854,0.00009747481,0.0001204382,0.0001406415],"category_scores_gemma":[0.005326286,0.00008811723,0.00002342614,0.0001633671,0.00006376724,0.0000939356,0.000005409953,0.0003929096,2.511646e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000417526,"about_ca_system_score_gemma":0.0007428583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001385812,"about_ca_topic_score_gemma":0.0152744,"domain_scores_codex":[0.9990195,0.0001381539,0.0004552041,0.00008737397,0.0001330493,0.0001667233],"domain_scores_gemma":[0.9976872,0.001231089,0.0003027259,0.00009050108,0.0002840432,0.0004044591],"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":[0.00001220308,0.000009886109,0.0008199009,0.0000583636,0.00001667139,0.0000269518,0.0009478446,0.00008959024,0.001705247,0.6754572,0.01320613,0.30765],"study_design_scores_gemma":[0.0002493967,0.00009919216,0.002780527,0.00006268798,0.00003362448,0.0001349076,0.00005858031,0.05321513,0.0006771176,0.9420514,0.0005392801,0.00009818455],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006867297,0.00007421652,0.992087,0.000176808,0.0003053639,0.00006732601,0.00006940105,0.000001937585,0.0003506459],"genre_scores_gemma":[0.05879669,0.000009195341,0.9410046,0.00003535009,0.00005934013,5.698202e-7,0.000001444317,0.00001197838,0.00008086078],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3075518,"threshold_uncertainty_score":0.8523473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06648699003897633,"score_gpt":0.3798288816548447,"score_spread":0.3133418916158684,"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."}}