{"id":"W4293070677","doi":"10.2196/38485","title":"Negative COVID-19 Vaccine Information on Twitter: Content Analysis","year":2022,"lang":"en","type":"article","venue":"JMIR Infodemiology","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Waterloo; McMaster University","funders":"","keywords":"Misinformation; Pandemic; Social media; Vaccination; Government (linguistics); Coronavirus disease 2019 (COVID-19); Medicine; Public health; Political science; Virology; World Wide Web; Computer science; Disease; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009913829,0.0001093538,0.0003092787,0.0003577188,0.0008661813,0.00002595275,0.000307307,0.00008468593,0.002546231],"category_scores_gemma":[0.0009411043,0.0001046091,0.0001630474,0.0008133496,0.00002000057,0.0002705286,0.0001532729,0.000288757,0.00008505309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004900574,"about_ca_system_score_gemma":0.0002390984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002670204,"about_ca_topic_score_gemma":0.0009715435,"domain_scores_codex":[0.9982484,0.0006253803,0.0004023513,0.000172815,0.000240374,0.0003106691],"domain_scores_gemma":[0.9986951,0.0005630929,0.0002726886,0.0002194507,0.00007425556,0.0001754383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003672069,0.00008960447,0.7439012,0.00001038905,0.0002761393,0.00001111973,0.06025477,0.01572498,0.00001199348,0.07165334,0.1065999,0.001099334],"study_design_scores_gemma":[0.00132331,0.0004370673,0.342999,0.000001489726,0.0001294327,0.000002551314,0.01619593,0.0006699202,0.000006968828,0.00889821,0.6290405,0.0002955498],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9446955,0.00003928521,0.001775934,0.03772358,0.0002862395,0.0005777142,0.0000357592,0.0001485077,0.01471754],"genre_scores_gemma":[0.9505424,0.00003133922,0.00006628632,0.04834963,0.00009229714,0.0002873139,0.00005974671,0.0000033236,0.0005676359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5224407,"threshold_uncertainty_score":0.9983656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09026226955581243,"score_gpt":0.3780310972798045,"score_spread":0.287768827723992,"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."}}