{"id":"W3175902990","doi":"","title":"H2oloo at TREC 2020: When all you got is a hammer... Deep Learning, Health Misinformation, and Precision Medicine.","year":2020,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Misinformation; Hammer; Computer science; Artificial intelligence; Deep learning; Data science; Engineering; Computer security; Mechanical engineering","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":[0.01149506,0.002348424,0.001345279,0.002667091,0.002032303,0.005002543,0.002520171,0.004764943,0.04682727],"category_scores_gemma":[0.01750206,0.0004853339,0.001012189,0.00176016,0.001793906,0.004501351,0.003082178,0.004276824,0.01863619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004015107,"about_ca_system_score_gemma":0.007653739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0801805,"about_ca_topic_score_gemma":0.2027159,"domain_scores_codex":[0.9969049,0.001304041,0.000121544,0.0002847994,0.001006792,0.0003778348],"domain_scores_gemma":[0.9836233,0.006292259,0.0006661137,0.00100912,0.004869177,0.003539946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001202866,0.00002732696,0.0002638735,0.0002214678,0.0000293159,0.00001209293,0.0000161301,0.0004036861,0.0002245507,0.0002845316,0.9913121,0.007084661],"study_design_scores_gemma":[0.001433197,0.0005151397,0.0118705,0.001200703,0.000290029,0.0001574872,0.0006167041,0.01612787,0.005188035,0.02174767,0.9405427,0.0003099946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01727866,0.03688133,0.01924152,0.171645,0.06058912,0.001493132,0.5981041,0.01110668,0.08366036],"genre_scores_gemma":[0.08486571,0.007941765,0.02941872,0.04291128,0.01551072,0.001127663,0.6851285,0.002914323,0.1301813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0801805,"threshold_uncertainty_score":0.1594276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05694637064717668,"score_gpt":0.2868362349313853,"score_spread":0.2298898642842086,"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."}}