{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006319039,0.0002389876,0.0003788315,0.0000699812,0.0002342832,0.0001718861,0.0007891954,0.0001114909,0.0006057622],"category_scores_gemma":[0.0005409115,0.0002071604,0.00004489314,0.0003151171,0.00009984105,0.0005652618,0.0005577766,0.0003717801,0.000194512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001020728,"about_ca_system_score_gemma":0.0001796909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001697681,"about_ca_topic_score_gemma":0.00002625291,"domain_scores_codex":[0.9975805,0.0001451875,0.0005921987,0.0006357064,0.0006332751,0.0004130797],"domain_scores_gemma":[0.9984088,0.0001566281,0.0003094547,0.0004645536,0.0002044512,0.0004561273],"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":[0.0002298518,0.00003675749,0.002519054,0.0002807409,0.00006180203,0.00002196357,0.1673732,0.0002050579,0.001037271,0.01267351,0.0334364,0.7821245],"study_design_scores_gemma":[0.001213626,0.0008684453,0.001934793,0.0001440489,0.00001466028,0.00004595765,0.0003867362,0.8611636,0.0005122804,0.00237281,0.1309355,0.0004075581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04166699,0.002195487,0.8680221,0.08236996,0.0003023971,0.0004827006,0.000004354772,0.0003574382,0.004598543],"genre_scores_gemma":[0.9720333,0.000884134,0.014446,0.01021141,0.0001701182,0.000003808174,0.00001852001,0.00001628291,0.002216382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9303663,"threshold_uncertainty_score":0.8447754,"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."}}