{"id":"W2942409160","doi":"10.1101/618108","title":"Best-compromise nutritional menus for childhood obesity","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Obesity, Physical Activity, Diet","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Comisión Nacional de Investigación Científica y Tecnológica","keywords":"Childhood obesity; Obesity; Metaheuristic; Computer science; Minification; Medicine; Risk analysis (engineering); Gerontology; Mathematical optimization; Mathematics; Artificial intelligence; Overweight","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007602836,0.0007505446,0.00057972,0.001076344,0.000457723,0.0009863507,0.0006236085,0.0009317696,0.005589029],"category_scores_gemma":[0.002563311,0.0003640399,0.0009587548,0.0006432518,0.0003949056,0.0007165758,0.000870651,0.0006920838,0.000247629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001000375,"about_ca_system_score_gemma":0.001394626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005441344,"about_ca_topic_score_gemma":0.005047397,"domain_scores_codex":[0.9996917,0.0001486852,0.00001049996,0.00003841582,0.00005294981,0.00005782318],"domain_scores_gemma":[0.9992224,0.0005236823,0.00009433148,0.00003289113,0.00007085208,0.00005584075],"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":[0.00008415572,0.00008268351,0.0008302617,0.00006905897,0.00002131224,0.00007990943,0.0000245378,0.9841487,0.0005102714,0.004588365,0.0006385986,0.008922104],"study_design_scores_gemma":[0.00002457506,0.00007877282,0.0003358505,0.0000260539,0.00001602227,0.00003772503,0.00005698288,0.9939309,0.0004310163,0.004218711,0.0008357843,0.000007588932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4168274,0.001646697,0.5466492,0.001689273,0.0001822191,0.0003626484,0.001202867,0.0006228819,0.03081681],"genre_scores_gemma":[0.8214076,0.0004609676,0.1733142,0.0001142011,0.00001856502,0.0001816005,0.0003204232,0.0000732689,0.004109108],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005589029,"threshold_uncertainty_score":0.0186972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0159947124254534,"score_gpt":0.2405256290195845,"score_spread":0.2245309165941311,"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."}}