{"id":"W1970257950","doi":"10.1155/2012/983147","title":"Cry-Based Classification of Healthy and Sick Infants Using Adapted Boosting Mixture Learning Method for Gaussian Mixture Models","year":2012,"lang":"en","type":"article","venue":"Modelling and Simulation in Engineering","topic":"Infant Health and Development","field":"Health Professions","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Bill and Melinda Gates Foundation","keywords":"Mixture model; Pattern recognition (psychology); Mel-frequency cepstrum; Boosting (machine learning); Binary classification; Artificial intelligence; Classifier (UML); Binary number; Gaussian; Computer science; Support vector machine; Machine learning; Feature extraction; Mathematics","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":[0.001374583,0.0001618803,0.0002939135,0.0002580232,0.0003539057,0.000007136777,0.00003343776,0.0002538651,0.000003059502],"category_scores_gemma":[0.0001461368,0.0001637289,0.00002423784,0.000182353,0.000008644064,0.0002110402,0.00001770262,0.0004407581,2.360281e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009816456,"about_ca_system_score_gemma":0.0001061896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008454683,"about_ca_topic_score_gemma":0.000004511829,"domain_scores_codex":[0.9983829,0.0001339041,0.0006497828,0.0002080298,0.0001389582,0.0004864222],"domain_scores_gemma":[0.9983557,0.001029253,0.000238684,0.00009751941,0.000122878,0.0001559001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001099409,0.00001306108,0.007382759,0.0009744062,0.000005122474,8.694549e-8,0.004679779,0.9848054,0.0003659654,0.0007834181,0.000001298599,0.0008787811],"study_design_scores_gemma":[0.000835743,0.00002598913,0.001590503,0.0004935675,0.00001198076,4.044649e-7,0.0002930255,0.9960883,0.00002668346,0.0001260811,0.0003488014,0.0001589436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2917088,0.0004484388,0.7070916,0.0000618331,0.0001343065,0.0004595539,0.000004779878,0.00004486456,0.00004579946],"genre_scores_gemma":[0.7687235,0.00002130646,0.2309797,0.00007796635,0.0001015803,0.00003175016,0.00002784112,0.00002570523,0.00001071823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4770147,"threshold_uncertainty_score":0.6676669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1048328921332956,"score_gpt":0.396774565793854,"score_spread":0.2919416736605583,"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."}}