{"id":"W2947689917","doi":"","title":"KlickLabs at TREC 2018 Precision Medicine track.","year":2018,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Track (disk drive); Precision medicine; Information retrieval; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003899843,0.0002101245,0.0002472667,0.00004839081,0.0001620823,0.00001941343,0.0003976635,0.0003340114,0.001396891],"category_scores_gemma":[0.00094206,0.0001563378,0.00005765753,0.0001843366,0.00110415,0.000003471679,0.0002335012,0.0001339317,0.0003641895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002824224,"about_ca_system_score_gemma":0.00009629839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003117627,"about_ca_topic_score_gemma":0.00007406814,"domain_scores_codex":[0.9984437,0.00008183625,0.0002910791,0.000528914,0.0003017106,0.0003527443],"domain_scores_gemma":[0.9989142,0.00006055315,0.0001085431,0.0005251367,0.0002163527,0.0001751696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001049243,0.00006164181,0.00135852,0.00002160658,0.00005201858,0.000008524553,0.0004011469,1.213394e-7,0.7493186,0.0002526328,0.09598885,0.1514871],"study_design_scores_gemma":[0.001302617,0.003580634,0.01065941,0.00009904224,0.00003584804,0.00005494713,0.0001531246,0.0001083822,0.3600321,0.0005778201,0.6229948,0.000401278],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9674541,0.001355243,0.003526289,0.001532356,0.0007376607,0.0001760509,0.0000199683,0.00008702403,0.02511132],"genre_scores_gemma":[0.9821305,0.0003029467,0.0007399212,0.0004500852,0.0008527071,0.00000355632,0.00006434168,0.00001728988,0.01543862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5270059,"threshold_uncertainty_score":0.999516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04302524158150629,"score_gpt":0.3146855815370476,"score_spread":0.2716603399555413,"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."}}