{"id":"W3109962547","doi":"10.18280/ria.340514","title":"Predicting Kids Malnutrition Using Multilayer Perceptron with Stochastic Gradient Descent","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Child Nutrition and Water Access","field":"Nursing","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stochastic gradient descent; Perceptron; Artificial intelligence; Classifier (UML); Computer science; Multilayer perceptron; Feature selection; Machine learning; Gradient descent; Malnutrition; Pattern recognition (psychology); Artificial neural network; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000119548,0.0002362361,0.0002471533,0.00009997958,0.0003032231,0.0001255868,0.0002203597,0.00007905138,0.0002339869],"category_scores_gemma":[0.00006937376,0.0002210356,0.000103515,0.0004220858,0.0001022453,0.0002544662,0.00005802659,0.0002878453,0.0001564083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001357095,"about_ca_system_score_gemma":0.00001267785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007237606,"about_ca_topic_score_gemma":0.00001387768,"domain_scores_codex":[0.9982808,0.00006876285,0.0004359943,0.0005335535,0.0002459504,0.0004349895],"domain_scores_gemma":[0.9991999,0.00006518245,0.0001203777,0.000247095,0.0001213709,0.0002460825],"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":[0.003389153,0.001646279,0.03759656,0.001218074,0.00009317877,0.00009867006,0.02899142,0.8149196,0.09557982,0.0002905792,0.001407249,0.01476945],"study_design_scores_gemma":[0.0002964235,0.0004852321,0.000599131,0.0004918213,0.00006784539,0.00007095499,0.001672063,0.8912295,0.1040535,0.00009362758,0.0005863492,0.0003535789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8403909,0.0001628817,0.1546427,0.003521496,0.0003570963,0.000589946,0.00001418215,0.0001949315,0.0001259111],"genre_scores_gemma":[0.9961407,0.000007731238,0.002379618,0.0008891129,0.0004698541,0.00001797888,0.00002259232,0.000046487,0.00002590411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1557499,"threshold_uncertainty_score":0.9013572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06206938319488028,"score_gpt":0.2865220532015832,"score_spread":0.2244526700067029,"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."}}