{"id":"W4220875893","doi":"10.3389/frai.2021.550603","title":"Planning as Inference in Epidemiological Dynamics Models","year":2022,"lang":"en","type":"article","venue":"Frontiers in Artificial Intelligence","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; University of British Columbia; Canadian Institute for Advanced Research","funders":"Natural Sciences and Engineering Research Council of Canada; Air Force Research Laboratory; Advanced Research Projects Agency; Canadian Institute for Advanced Research; Western Canada Research Grid; U.S. Air Force; Compute Canada; Defense Advanced Research Projects Agency","keywords":"Inference; Computer science; Fiducial inference; Machine learning; Data science; Risk analysis (engineering); Causal inference; Bayesian inference; Artificial intelligence; Operations research; Management science; Frequentist inference; Bayesian probability; Econometrics; Engineering; Medicine; Economics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005446309,0.001104517,0.00104913,0.001163262,0.000890566,0.002782828,0.002251233,0.00160602,0.00498894],"category_scores_gemma":[0.01995828,0.001019331,0.001990008,0.001223983,0.002866217,0.004267057,0.002711011,0.003797777,0.0005940322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002394442,"about_ca_system_score_gemma":0.003087844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01138907,"about_ca_topic_score_gemma":0.009337015,"domain_scores_codex":[0.9968052,0.001830818,0.0001843416,0.000531291,0.0004928723,0.0001555246],"domain_scores_gemma":[0.9840874,0.01392825,0.0005986135,0.000776651,0.0004320542,0.0001770205],"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":[0.00003207192,0.00003187868,0.0005303308,0.0001146573,0.00005027297,0.00009954865,0.0002017604,0.5773894,0.0002611974,0.4062093,0.0007544215,0.01432525],"study_design_scores_gemma":[0.00001136883,0.00001028163,0.00004998741,0.00002221347,0.00001468998,0.00001776346,0.00002433006,0.6288859,0.0002914837,0.3685451,0.002115539,0.00001134912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003133423,0.0001708392,0.9928412,0.0008206886,0.00003068553,0.00003441975,0.0001180464,0.0002865894,0.002564139],"genre_scores_gemma":[0.2540365,0.0008668986,0.7392613,0.0004132321,0.0001274698,0.0003596141,0.0004574307,0.000184741,0.00429278],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01138907,"threshold_uncertainty_score":0.02880323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4136120602748449,"score_gpt":0.4667121634932821,"score_spread":0.05310010321843717,"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."}}