{"id":"W4366078185","doi":"10.5210/disco.v5i0.2680","title":"MLTrends: Graphing MEDLINE term usage over time","year":2010,"lang":"en","type":"article","venue":"Journal of Biomedical Discovery and Collaboration","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital","funders":"","keywords":"MEDLINE; Computer science; Term (time); Information retrieval; Web of science","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":[0.000379676,0.0001025855,0.0001658102,0.00009653554,0.00007452286,0.00008787601,0.0001270995,0.0002265809,0.00004475653],"category_scores_gemma":[0.0003394539,0.00007318181,0.00006135436,0.0002062474,0.0002916951,0.00002844501,0.00004932468,0.0002047988,0.000001642838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004827084,"about_ca_system_score_gemma":0.0001134477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001670786,"about_ca_topic_score_gemma":0.00001204134,"domain_scores_codex":[0.999119,0.00004331222,0.0003154112,0.000140692,0.00024995,0.0001315868],"domain_scores_gemma":[0.9994285,0.00002683207,0.000202953,0.0001106872,0.00008691789,0.000144082],"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":[0.0001404023,0.0001004636,0.001329961,0.00001300517,0.00004926427,0.0000153276,0.00005975135,3.881634e-7,0.9606916,0.00009309588,0.008119103,0.02938764],"study_design_scores_gemma":[0.00751413,0.006213999,0.1104873,0.0003082489,0.0003091322,0.0009345489,0.0008299026,0.0009348394,0.1911924,0.0022831,0.6778786,0.001113821],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962389,0.0004888159,0.001484022,0.001056302,0.0005316958,0.000034877,0.00002886031,0.000005569695,0.000130956],"genre_scores_gemma":[0.9968889,0.00024807,0.00135627,0.0002729182,0.0008249535,0.000001220983,0.00006126924,0.000007876942,0.0003385055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7694992,"threshold_uncertainty_score":0.2984268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003948985190073336,"score_gpt":0.2569139647097169,"score_spread":0.2529649795196435,"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."}}