{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003519686,0.0002869155,0.000830908,0.000295575,0.0002586093,0.00002104718,0.0007300735,0.0001482121,0.0001835623],"category_scores_gemma":[0.01875236,0.0002642967,0.0001283741,0.0008682079,0.000262091,0.0001322538,0.000759998,0.001065372,0.00001794804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009906603,"about_ca_system_score_gemma":0.00007568867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005315393,"about_ca_topic_score_gemma":0.0002964754,"domain_scores_codex":[0.9961355,0.0008739682,0.001240218,0.000693661,0.0003428944,0.0007136983],"domain_scores_gemma":[0.9931231,0.00612134,0.0002485107,0.0003735235,0.00003871886,0.00009479267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001706488,0.0002733303,0.0821443,0.00002499666,0.00001544605,0.0001194629,0.001452525,0.378694,0.000006184916,0.5141155,0.001321777,0.02166185],"study_design_scores_gemma":[0.00002046877,0.0000834189,0.0004450248,0.00002304981,0.000003716208,0.000001367808,0.002702564,0.4125792,0.0000186876,0.5837787,0.0001657044,0.0001781148],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1405036,0.0005929805,0.8531064,0.002009106,0.0008398167,0.000515977,0.00001980613,0.000132284,0.002279922],"genre_scores_gemma":[0.964863,0.00009602541,0.03368594,0.0009362239,0.0000466085,0.0002585383,0.000008514114,0.00001867549,0.00008647577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8243594,"threshold_uncertainty_score":0.9999809,"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."}}