{"id":"W4241029535","doi":"10.31219/osf.io/q9m4s","title":"Covid-19 and “Immune Boosting” on the Internet: A Content Analysis of Google Search Results","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Manitoba","funders":"Genome Alberta; Ministero dello Sviluppo Economico; Alberta Innovates; Government of Canada; Government of Alberta; Genome Canada","keywords":"Boosting (machine learning); Misinformation; Immune system; Web page; Artificial intelligence; Computer science; Medicine; World Wide Web; Immunology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002018145,0.0003332003,0.0005700227,0.02180104,0.0009668137,0.002838349,0.0004780415,0.000412425,0.001897372],"category_scores_gemma":[0.01769669,0.0001756893,0.0005458297,0.02978132,0.0009618161,0.002661475,0.001403239,0.0004505633,0.0004191682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003327826,"about_ca_system_score_gemma":0.003499133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1282917,"about_ca_topic_score_gemma":0.1803931,"domain_scores_codex":[0.9973522,0.000638833,0.000414275,0.0001770635,0.001127932,0.0002897091],"domain_scores_gemma":[0.974178,0.01547389,0.005459803,0.000342216,0.003865183,0.0006809304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008643284,0.0001755166,0.7673416,0.01263105,0.0004431935,0.001693234,0.08359967,0.0005386525,0.002804492,0.002437094,0.02660277,0.1008684],"study_design_scores_gemma":[0.00002036774,0.00007961199,0.899267,0.002705922,0.000248757,0.0008108381,0.06934851,0.001379823,0.000601046,0.0003428456,0.02512674,0.00006853825],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9678212,0.003655613,0.0003027031,0.0005463211,0.00002616846,0.0002070638,0.02191078,0.0000582775,0.00547181],"genre_scores_gemma":[0.9691685,0.005073032,0.002262157,0.0002874233,0.00005741114,0.0002788896,0.02104271,0.00006916795,0.001760626],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1282917,"threshold_uncertainty_score":0.2550899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3144105151196727,"score_gpt":0.4131894669880869,"score_spread":0.09877895186841418,"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."}}