{"id":"W4404815779","doi":"10.1093/pnasnexus/pgae540","title":"Modeling long-term nutritional behaviors using deep homeostatic reinforcement learning","year":2024,"lang":"en","type":"article","venue":"PNAS Nexus","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Japan Society for the Promotion of Science; Japan Society for the Promotion of Science London; Japan Agency for Medical Research and Development","keywords":"Term (time); Reinforcement; Reinforcement learning; Psychology; Cognitive psychology; Computer science; Artificial intelligence; Social psychology; Physics","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":[0.0004071062,0.0004217697,0.000368398,0.0002077747,0.0001987529,0.0005030691,0.0008101028,0.0006113412,0.001214954],"category_scores_gemma":[0.001120095,0.0002542885,0.0003073548,0.0001441905,0.0006935268,0.0005824735,0.0005792842,0.0006458972,0.0001222228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007182919,"about_ca_system_score_gemma":0.0006427486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006674022,"about_ca_topic_score_gemma":0.006421089,"domain_scores_codex":[0.999897,0.00002775922,0.00000451184,0.00002653215,0.00001819298,0.00002597717],"domain_scores_gemma":[0.9996994,0.0001212267,0.00007457641,0.00002062457,0.00005184838,0.00003232962],"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":[0.00001922508,0.00002293547,0.001036828,0.000009455525,0.00001572152,0.00002668533,0.0000154078,0.9913018,0.0008739238,0.002996785,0.000126354,0.003554837],"study_design_scores_gemma":[0.000001446068,0.000003727885,0.00006954289,5.280946e-7,0.000001068684,0.000001521662,0.00000126396,0.9990778,0.00004979423,0.0007655877,0.00002690043,7.793441e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4064566,0.0003579902,0.5845053,0.0005429207,0.00005834393,0.00004960792,0.0001020713,0.0004152581,0.007511937],"genre_scores_gemma":[0.987021,0.00006023212,0.01111954,0.00004529257,0.000007338481,0.00003986996,0.00002991265,0.00001414093,0.001662735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006674022,"threshold_uncertainty_score":0.01327038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03408225842641407,"score_gpt":0.2961966146611672,"score_spread":0.2621143562347531,"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."}}