{"id":"W4284878824","doi":"10.2196/40365","title":"Correction:The Associations Between Racially/Ethnically Stratified COVID-19 Tweets and COVID-19 Cases and Deaths: Cross-sectional Study (Preprint)","year":2022,"lang":"en","type":"erratum","venue":"JMIR Formative Research","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Preprint; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Ethnically diverse; Cross-sectional study; Medicine; Virology; Computer science; World Wide Web; Environmental health; Disease; Pathology; Infectious disease (medical specialty); Population","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0142804,0.003256052,0.00232415,0.006268177,0.004344685,0.004969089,0.005229782,0.005448768,0.1813739],"category_scores_gemma":[0.2614166,0.001813539,0.002468868,0.006128552,0.002675845,0.003493669,0.003645902,0.009682138,0.06700873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004254831,"about_ca_system_score_gemma":0.01183861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03945937,"about_ca_topic_score_gemma":0.0370046,"domain_scores_codex":[0.9860036,0.003029446,0.00304742,0.001594965,0.005526749,0.0007978615],"domain_scores_gemma":[0.8717241,0.04376308,0.005553452,0.01065759,0.0651639,0.003137838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00003286465,0.000003175709,0.0001063882,0.0001644182,0.0000180848,0.0001037729,0.00005445837,0.00002839244,0.00002081806,0.0004911416,0.9965245,0.002452007],"study_design_scores_gemma":[0.0001545353,0.00003103385,0.001980517,0.001708544,0.0001412843,0.000695443,0.0003414287,0.0006394436,0.0004539122,0.004192732,0.9895364,0.0001248087],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0003018569,0.0005381192,0.001840509,0.05373947,0.9213171,0.00006256216,0.01754508,0.001410552,0.003244748],"genre_scores_gemma":[0.05024184,0.008188815,0.02554202,0.1261659,0.3325681,0.001409678,0.0464028,0.01474667,0.3947341],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1813739,"threshold_uncertainty_score":0.6067562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2480628449235206,"score_gpt":0.5622583244480178,"score_spread":0.3141954795244972,"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."}}