{"id":"W4413571136","doi":"10.64628/aam.ajhkn5ugy","title":"How Canadians can use social media to help debunk COVID-19 misinformation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Misinformation; Coronavirus disease 2019 (COVID-19); Social media; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Psychology; Internet privacy; Sociology; Advertising; Social psychology; Business; Computer science; Medicine; Virology; Outbreak; World Wide Web; Computer security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00220942,0.0003129456,0.0002626196,0.002025022,0.01756113,0.008681044,0.001105575,0.002424724,0.02741692],"category_scores_gemma":[0.01339037,0.0002681165,0.0005008947,0.003344539,0.004562108,0.003211599,0.00247825,0.002645998,0.00205535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03823129,"about_ca_system_score_gemma":0.07715183,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9859159,"about_ca_topic_score_gemma":0.9929101,"domain_scores_codex":[0.9967511,0.0008377885,0.00004772166,0.0001998909,0.0009297028,0.001233865],"domain_scores_gemma":[0.9899487,0.001410792,0.0007561393,0.0003696873,0.00477353,0.002741066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005212441,0.001003987,0.2625845,0.0002587416,0.0001834975,0.001598511,0.1516686,0.0008890217,0.00106379,0.09093016,0.3167042,0.1725937],"study_design_scores_gemma":[0.0001167098,0.0001377202,0.1179693,0.0005383732,0.0001890419,0.000262941,0.3086931,0.001140945,0.0006164715,0.01017687,0.5599545,0.000204105],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"commentary","genre_scores_codex":[0.4329272,0.002319596,0.00117542,0.09779578,0.0009116162,0.0001556482,0.00119649,0.00009737705,0.4634208],"genre_scores_gemma":[0.9265646,0.001700394,0.0007223651,0.006257962,0.0000955551,0.00002998222,0.0003049516,0.00003954506,0.0642846],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.03823129,"threshold_uncertainty_score":0.2773888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1006617235024668,"score_gpt":0.3399804480464136,"score_spread":0.2393187245439468,"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."}}