{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001033465,0.0007481182,0.0007087897,0.0007562779,0.0002557725,0.0006092417,0.0006082525,0.0006546888,0.001104769],"category_scores_gemma":[0.001596598,0.000341282,0.0009627109,0.0005975147,0.0001759518,0.0005410062,0.0004282846,0.0009232712,0.000362213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005433573,"about_ca_system_score_gemma":0.0006876653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01588959,"about_ca_topic_score_gemma":0.01323705,"domain_scores_codex":[0.9996589,0.00009894041,0.00003684989,0.00008060369,0.0000623537,0.0000623669],"domain_scores_gemma":[0.9994951,0.0002660824,0.00005297395,0.00002573759,0.0001308865,0.00002898992],"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":[0.0003474294,0.0005838281,0.0478579,0.0001360094,0.0002568257,0.0002992765,0.00008166913,0.8171995,0.00255908,0.000668001,0.005763391,0.1242472],"study_design_scores_gemma":[0.000004721961,0.00004735691,0.00266436,0.000006847205,0.00001131793,0.00001522389,0.00001238217,0.9963866,0.0003638806,0.0002969,0.0001849878,0.000005358404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6264661,0.001645245,0.3608693,0.001414972,0.0003338433,0.0001624816,0.002532483,0.002675844,0.003899741],"genre_scores_gemma":[0.9521981,0.0003303926,0.04299625,0.0001265325,0.00004540073,0.00008358462,0.001782079,0.0000318953,0.002405764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01588959,"threshold_uncertainty_score":0.03159422,"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."}}