{"id":"W1997441005","doi":"10.1109/iccabs.2012.6182651","title":"Identifying cancer biomarkers by knowledge discovery from medical literature","year":2012,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Data science; Field (mathematics); Flexibility (engineering); Knowledge extraction; Domain (mathematical analysis); Information retrieval; Cancer; Information extraction; Range (aeronautics); Association (psychology); Data mining; Medicine; Engineering; Psychology","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":[0.0001815726,0.0001358901,0.0001240443,0.00002180733,0.00005635232,0.00005083295,0.0002162878,0.0003793241,0.0003092725],"category_scores_gemma":[0.0001600668,0.00009812076,0.00007778095,0.00009595826,0.0001429969,0.00000870853,0.0001643019,0.0001237895,0.00002630323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001173037,"about_ca_system_score_gemma":0.00005800937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001051915,"about_ca_topic_score_gemma":0.0001044495,"domain_scores_codex":[0.9990589,0.00005625673,0.0001475018,0.0002487302,0.0001691071,0.0003195149],"domain_scores_gemma":[0.9994941,0.00003123011,0.00003375308,0.0001988927,0.00002358951,0.0002184128],"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":[0.00007011965,0.0002341658,0.03485909,0.00003646192,0.0003443962,0.000004714824,0.0004538967,3.157252e-8,0.2615137,0.0001008852,0.5992293,0.1031533],"study_design_scores_gemma":[0.0008772989,0.00007689149,0.009492728,0.0001971341,0.00004750336,0.00001407338,0.0005643214,0.00002930402,0.1944993,0.00008774822,0.7936108,0.0005029896],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8275419,0.1555429,0.008735636,0.001024848,0.00169001,0.00007868277,0.000174003,0.00005993706,0.005152062],"genre_scores_gemma":[0.9895902,0.001905487,0.0009590709,0.0006924306,0.0009970787,0.00002173967,0.0004347198,0.00001672628,0.00538255],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1943814,"threshold_uncertainty_score":0.4001249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01695153797487494,"score_gpt":0.3214199082815851,"score_spread":0.3044683703067101,"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."}}