{"id":"W3204245871","doi":"10.5195/jmla.2021.1173","title":"Mapping the biomedical sciences using Medical Subject Headings: a comparison between MeSH co-assignments and MeSH citation pairs","year":2021,"lang":"en","type":"article","venue":"Journal of the Medical Library Association JMLA","topic":"Academic Writing and Publishing","field":"Arts and Humanities","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Citation; Subject (documents); Computer science; Information retrieval; Citation analysis; Data science; World Wide Web","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","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.009179323,0.001010804,0.0008649926,0.04942009,0.001028397,0.004747421,0.000883816,0.0007425623,0.005606649],"category_scores_gemma":[0.07591042,0.0003501345,0.0009922278,0.04717232,0.0009928471,0.005313248,0.004202202,0.0006985049,0.001101252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001024873,"about_ca_system_score_gemma":0.001119432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002195849,"about_ca_topic_score_gemma":0.002755078,"domain_scores_codex":[0.9914451,0.004200895,0.0009455716,0.0008894915,0.002296114,0.000222969],"domain_scores_gemma":[0.9352175,0.04791201,0.00835877,0.002444814,0.005408335,0.0006585384],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002280177,0.0002119969,0.2535462,0.01493736,0.003248312,0.0006051391,0.01813982,0.01433763,0.009817843,0.0444334,0.01879563,0.6196465],"study_design_scores_gemma":[0.0003630086,0.0007492683,0.589125,0.004305077,0.002559072,0.002312441,0.0318006,0.07744019,0.01113106,0.1447432,0.1349373,0.0005337704],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7148673,0.0228992,0.1705029,0.003400794,0.0007584331,0.0009673845,0.02788232,0.003762059,0.0549596],"genre_scores_gemma":[0.8870292,0.004822288,0.09823851,0.0001233233,0.0002035814,0.0007004384,0.006896107,0.0003659356,0.001620727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9908207,"threshold_uncertainty_score":0.04854548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06977240929260138,"score_gpt":0.2969864384258571,"score_spread":0.2272140291332558,"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."}}