{"id":"W4391070609","doi":"10.1016/j.jbi.2024.104588","title":"Semantics-enabled biomedical literature analytics","year":2024,"lang":"en","type":"editorial","venue":"Journal of Biomedical Informatics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"U.S. National Library of Medicine; National Institutes of Health","keywords":"Computer science; Analytics; Semantics (computer science); Data science; Programming language","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01253812,0.002110837,0.002683418,0.01007583,0.002139575,0.01335821,0.003055484,0.007529995,0.00836453],"category_scores_gemma":[0.03646446,0.00149541,0.002264459,0.003650322,0.003435675,0.009076821,0.003200978,0.01326382,0.004549611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002730972,"about_ca_system_score_gemma":0.004272517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001942009,"about_ca_topic_score_gemma":0.005262535,"domain_scores_codex":[0.9930397,0.001639109,0.0009765019,0.0006616899,0.003443417,0.0002395494],"domain_scores_gemma":[0.9483163,0.03108946,0.00153437,0.001214357,0.01472136,0.003124127],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005391521,0.0000151145,0.00003867471,0.0003335147,0.00005969817,0.0000955347,0.00002262501,0.0001252288,0.0001375561,0.002219852,0.9801128,0.01678539],"study_design_scores_gemma":[0.0000908186,0.0000199104,0.0002008933,0.0006824822,0.0001302503,0.0002290769,0.00005057888,0.001926769,0.0004621593,0.01718988,0.9789781,0.00003925298],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0001378874,0.01152115,0.004881071,0.0876063,0.8919681,0.00004061201,0.0005663956,0.0005683406,0.00271004],"genre_scores_gemma":[0.002553738,0.0141741,0.004404866,0.02112786,0.94478,0.00005856427,0.0005592814,0.0002342739,0.01210729],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.9874619,"threshold_uncertainty_score":0.06630874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007967348372261453,"score_gpt":0.2849394155078475,"score_spread":0.276972067135586,"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."}}