{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.002070729,0.001176302,0.0007545268,0.02334238,0.0005143182,0.002725456,0.0007411393,0.000608829,0.01349043],"category_scores_gemma":[0.01343445,0.0005170188,0.0013186,0.02258929,0.0003103965,0.003944764,0.001575,0.0009551157,0.004348718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008979996,"about_ca_system_score_gemma":0.001285879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006905898,"about_ca_topic_score_gemma":0.009472819,"domain_scores_codex":[0.9984906,0.0003007641,0.0003599809,0.0003252128,0.0004485845,0.00007488696],"domain_scores_gemma":[0.9886491,0.007213416,0.001608611,0.000907652,0.001376819,0.0002443912],"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":[0.001094346,0.0003122342,0.04972358,0.005591067,0.0008965124,0.000656476,0.004267336,0.01152931,0.01406066,0.01599961,0.2225871,0.6732817],"study_design_scores_gemma":[0.000333763,0.0008752237,0.124508,0.0009649586,0.000654594,0.001939607,0.002555902,0.151102,0.03155489,0.03733095,0.6477553,0.0004248351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.1139806,0.00313663,0.1749331,0.001613979,0.0004752414,0.001053805,0.4541802,0.2348417,0.01578479],"genre_scores_gemma":[0.2195899,0.004546588,0.4513044,0.0003778281,0.0002728512,0.001966412,0.2977004,0.01528026,0.008961459],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9766576,"threshold_uncertainty_score":0.04513001,"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."}}