{"id":"W4205697470","doi":"10.1073/pnas.2111452118","title":"An open repository of real-time COVID-19 indicators","year":2021,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Center for Machine Learning and Health, School of Computer Science, Carnegie Mellon University; Centers for Disease Control and Prevention; Amazon Web Services; National Science Foundation","keywords":"Coronavirus disease 2019 (COVID-19); Social distance; Pandemic; Public health; Internet privacy; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; The Internet; Population; Data science; Computer science; Actuarial science; Medicine; Business; Environmental health; World Wide Web; 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":[],"consensus_categories":[],"category_scores_codex":[0.001555287,0.00007241614,0.0002444095,0.0001578857,0.0001370367,0.00002204896,0.001089969,0.00006196111,0.00006521573],"category_scores_gemma":[0.001585972,0.00005146039,0.0000570292,0.001083275,0.0009803819,0.0004483104,0.0003195275,0.00009887533,0.000001501893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005703968,"about_ca_system_score_gemma":0.0005098908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002556362,"about_ca_topic_score_gemma":6.546691e-8,"domain_scores_codex":[0.9979631,0.00001718238,0.0003786673,0.0002912589,0.00123946,0.0001102998],"domain_scores_gemma":[0.9987651,0.0001199346,0.0005787316,0.00002445388,0.0003708167,0.0001410341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00007191086,0.0002062451,0.1291214,0.0002112837,0.00003639064,1.507107e-7,0.0002725505,0.00001717721,0.8556034,0.00930403,0.004995958,0.0001595243],"study_design_scores_gemma":[0.00049923,0.0001184883,0.5109497,0.0001584768,0.00003217291,0.00004161844,0.0003433043,0.0002303461,0.4795605,0.00659568,0.001381625,0.00008881227],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9648355,0.00008028695,2.346775e-7,0.00364432,0.00001349046,0.0002240813,0.00008794018,0.00001643257,0.03109767],"genre_scores_gemma":[0.9968917,0.000029562,0.001915651,0.0006434499,0.00004902359,0.00000694962,0.000002302506,0.000004291369,0.0004570357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3818283,"threshold_uncertainty_score":0.3612258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04320088745258147,"score_gpt":0.3684871504465092,"score_spread":0.3252862629939277,"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."}}