{"id":"W3154793975","doi":"10.1145/3404835.3463120","title":"Vera: Prediction Techniques for Reducing Harmful Misinformation in Consumer Health Search","year":2021,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Misinformation; Computer science; Credibility; Information retrieval; Relevance (law); Ranking (information retrieval); Context (archaeology); Metric (unit); Data science; Computer security","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003760484,0.001752802,0.001316382,0.003646908,0.0008831481,0.001505086,0.001725665,0.001984364,0.003590916],"category_scores_gemma":[0.01537582,0.0005510516,0.001376674,0.001845693,0.0006716198,0.004610531,0.001332571,0.002673572,0.003406584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00101542,"about_ca_system_score_gemma":0.002041195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0101488,"about_ca_topic_score_gemma":0.01808499,"domain_scores_codex":[0.9981846,0.0007309113,0.000114342,0.000394434,0.0004283182,0.0001473059],"domain_scores_gemma":[0.9903969,0.006456738,0.0004515244,0.001115209,0.001330525,0.0002492019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001223171,0.0009764932,0.0100851,0.0006137083,0.0002605029,0.0002439292,0.0006569972,0.06814682,0.0140234,0.005922411,0.04565767,0.8521899],"study_design_scores_gemma":[0.00008522337,0.0003902855,0.001536661,0.00003896731,0.0001226827,0.000195232,0.0001162997,0.9746629,0.007195649,0.01040034,0.005212441,0.00004324738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2170847,0.009532049,0.7056726,0.004229609,0.001009461,0.001028821,0.004107203,0.04399586,0.01333968],"genre_scores_gemma":[0.7319327,0.001622458,0.2419599,0.001013806,0.0007237368,0.0003729908,0.006828506,0.001121549,0.01442431],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0101488,"threshold_uncertainty_score":0.02017945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04702965344254133,"score_gpt":0.3218547508486092,"score_spread":0.2748250974060678,"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."}}