{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001415323,0.0001773872,0.000170813,0.000667338,0.0004572859,0.001067543,0.0003032621,0.0004879774,0.001117033],"category_scores_gemma":[0.005374482,0.0001848885,0.0002052997,0.000919181,0.0005071234,0.0007299971,0.0008394707,0.000362257,0.0001344118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001471078,"about_ca_system_score_gemma":0.0005765614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04392269,"about_ca_topic_score_gemma":0.07364497,"domain_scores_codex":[0.9989998,0.0005868121,0.00004056064,0.0001224672,0.00009815784,0.000152218],"domain_scores_gemma":[0.9977965,0.0006174987,0.0008078602,0.0002364809,0.0003740236,0.0001676458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000479683,0.0002105849,0.9620002,0.00004663086,0.0001303961,0.0001022469,0.001932078,0.006410536,0.002047083,0.002449359,0.001827048,0.02236413],"study_design_scores_gemma":[0.000005198371,0.0001341879,0.991147,0.00002403585,0.00001769466,0.00004952622,0.002512936,0.00373585,0.0004936199,0.0003997565,0.001466673,0.00001357514],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975125,0.0001071991,0.0001428513,0.0001752483,0.000003199643,0.000006534933,0.0002460081,0.000003223951,0.001803356],"genre_scores_gemma":[0.9994994,0.00005882823,0.000135974,0.00002457157,0.000001964539,0.000004738082,0.0001164599,0.00000139683,0.0001566833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04392269,"threshold_uncertainty_score":0.0873341,"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."}}