{"id":"W4319072347","doi":"10.2196/45742","title":"Correction: Social Media Monitoring of the COVID-19 Pandemic and Influenza Epidemic With Adaptation for Informal Language in Arabic Twitter Data: Qualitative Study","year":2023,"lang":"en","type":"erratum","venue":"JMIR Medical Informatics","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Arabic; Social media; Adaptation (eye); Computer science; 2019-20 coronavirus outbreak; Virology; Data science; World Wide Web; Linguistics; Medicine; Biology; Infectious disease (medical specialty); Pathology","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":[0.0112121,0.0020023,0.001535754,0.002984598,0.005443912,0.004993134,0.003344359,0.006770104,0.04633727],"category_scores_gemma":[0.2076296,0.0009106915,0.001240196,0.002794256,0.004018749,0.004331173,0.003508511,0.01141529,0.0208724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006041074,"about_ca_system_score_gemma":0.01550129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05008718,"about_ca_topic_score_gemma":0.0520865,"domain_scores_codex":[0.9886417,0.003240699,0.002307381,0.001056161,0.004129068,0.0006249589],"domain_scores_gemma":[0.8912655,0.04324006,0.004243581,0.003912783,0.0541401,0.003197906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001309261,0.000003139514,0.00006284852,0.0001544687,0.000004829669,0.0001262239,0.0002300603,0.00001440085,0.00001655869,0.0004853673,0.9969108,0.001978318],"study_design_scores_gemma":[0.00004527622,0.00002160876,0.0008178809,0.001653162,0.00004507919,0.0004396643,0.001526699,0.0002598957,0.00028029,0.00177393,0.9930595,0.0000770819],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0003561999,0.0006761595,0.001151279,0.2285574,0.7627754,0.0001029324,0.002639187,0.0004469844,0.003294587],"genre_scores_gemma":[0.04424737,0.009984159,0.01050643,0.4740644,0.2377556,0.00215444,0.004118617,0.00483615,0.2123329],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.05008718,"threshold_uncertainty_score":0.1550136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2252441089456258,"score_gpt":0.4838822956641144,"score_spread":0.2586381867184886,"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."}}