{"id":"W3216200900","doi":"10.1101/2021.11.23.21266775","title":"Recurring Spatiotemporal Patterns of COVID-19 in the United States","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Pittsburgh","keywords":"Geography; Coronavirus disease 2019 (COVID-19); Demography; Incidence (geometry); Common spatial pattern; Spatial ecology; Cartography; Statistics; Mathematics; Ecology; Medicine; Biology; Geometry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000523151,0.00008870972,0.0001860325,0.001070831,0.0002525835,0.0005520817,0.0001759433,0.0001704859,0.0005782452],"category_scores_gemma":[0.001575226,0.00008560489,0.0001731686,0.00148005,0.0001936176,0.0002640366,0.0004768676,0.0002348625,0.00007587588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005373178,"about_ca_system_score_gemma":0.0004561828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05774309,"about_ca_topic_score_gemma":0.09027181,"domain_scores_codex":[0.999744,0.00005248682,0.00003069001,0.00007175191,0.00005286872,0.00004804011],"domain_scores_gemma":[0.9991426,0.000156743,0.0003423475,0.00005905097,0.0002140197,0.00008518298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003403577,0.00001376135,0.9948918,0.00001092616,0.00003782374,0.0000412032,0.0001687532,0.0003861773,0.0003051332,0.0001070414,0.0005795563,0.003423763],"study_design_scores_gemma":[0.000001253204,0.00001057649,0.9977313,0.000008124677,0.00000721462,0.00002979977,0.0003714893,0.001160337,0.00007675446,0.00004150057,0.0005586331,0.000003001644],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974962,0.0001478825,0.0001837102,0.00009966925,0.000004923454,0.000006617111,0.00149175,0.0000107192,0.0005585779],"genre_scores_gemma":[0.9981729,0.00006561846,0.0001488845,0.00002654775,0.000005033227,0.000006995558,0.001493407,0.000002093286,0.00007849719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05774309,"threshold_uncertainty_score":0.114814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3391345480357092,"score_gpt":0.4486919706443549,"score_spread":0.1095574226086457,"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."}}