{"id":"W3199267540","doi":"10.1111/risa.13800","title":"The Efficiency of U.S. Public Space Utilization During the COVID‐19 Pandemic","year":2021,"lang":"en","type":"article","venue":"Risk Analysis","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Space (punctuation); Public health; Virology; Betacoronavirus; Public space; Environmental health; Environmental science; Political science; Computer science; Medicine; Engineering; Outbreak; Architectural engineering; Nursing","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002917423,0.0001204581,0.0004008849,0.00007507835,0.0008149659,0.00004128211,0.0003063268,0.0000606599,0.0001499963],"category_scores_gemma":[0.04415916,0.00005888382,0.0003925807,0.00225482,0.0002035095,0.00003207831,0.0002393935,0.0001637483,0.000008146434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001367165,"about_ca_system_score_gemma":0.00007399664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002998259,"about_ca_topic_score_gemma":0.002899386,"domain_scores_codex":[0.9977632,0.0009469254,0.000451125,0.0002734219,0.0003052866,0.0002600436],"domain_scores_gemma":[0.9902177,0.008468295,0.0004053434,0.0006461066,0.000188632,0.00007386843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005397287,0.00006029287,0.9829237,0.0000390578,0.001065556,0.000002205755,0.000500828,0.001615525,0.00008576542,0.0122579,0.0004293859,0.001014399],"study_design_scores_gemma":[0.0008319149,0.00004741935,0.6980492,0.00001790283,0.007927792,0.00001080895,0.005933419,0.04431827,0.0007274436,0.1897254,0.05183496,0.0005754352],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.88767,0.004233187,0.1011859,0.00627465,0.00003261105,0.0001359803,0.00001726301,0.00008413935,0.0003663231],"genre_scores_gemma":[0.9928365,0.006092748,0.0002913847,0.0001423978,0.00002877684,0.00001639812,0.000003846007,0.000006562531,0.0005813309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2848745,"threshold_uncertainty_score":0.9638923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3169695346883926,"score_gpt":0.4339380816143873,"score_spread":0.1169685469259947,"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."}}