{"id":"W4413551433","doi":"10.64628/aam.6r7qu7uu9","title":"Coronavirus: How Twitter could more effectively ease its impact","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brock University","funders":"","keywords":"Coronavirus; Coronavirus disease 2019 (COVID-19); Social media; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Internet privacy; Computer science; World Wide Web; Medicine","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":[0.002794489,0.0006430157,0.0003758355,0.001449029,0.001644765,0.007201751,0.0005954005,0.004790384,0.02483735],"category_scores_gemma":[0.01549366,0.0002479574,0.0004422549,0.001062656,0.001857291,0.01024274,0.002608114,0.002103098,0.005330107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001425548,"about_ca_system_score_gemma":0.001856865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004095878,"about_ca_topic_score_gemma":0.004078952,"domain_scores_codex":[0.9987483,0.0005996554,0.00005241354,0.0001405664,0.0002943799,0.0001646067],"domain_scores_gemma":[0.9932261,0.003910679,0.0005773588,0.0006581422,0.0009534454,0.0006742654],"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":[0.0008010704,0.0005312898,0.01336578,0.001229652,0.0002755702,0.0007796796,0.004801589,0.005014807,0.01058695,0.3378389,0.3851442,0.2396305],"study_design_scores_gemma":[0.0001572865,0.0002772229,0.01094935,0.0006588064,0.0002472922,0.0004316324,0.006633077,0.01616066,0.00947801,0.3601313,0.594699,0.0001762988],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"commentary","genre_scores_codex":[0.1175544,0.01020787,0.02740991,0.3845512,0.01733383,0.0001974744,0.002372938,0.001737697,0.4386347],"genre_scores_gemma":[0.8562289,0.006788676,0.0133545,0.02474421,0.008952844,0.000128809,0.0006361013,0.000408821,0.08875719],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.02483735,"threshold_uncertainty_score":0.08308917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1585629237563703,"score_gpt":0.4413167161486267,"score_spread":0.2827537923922563,"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."}}