{"id":"W2773129102","doi":"10.29173/jchla/jabsc.v38i3.29336","title":"Yale MeSH Analyzer","year":2017,"lang":"en","type":"article","venue":"Journal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques de la santé du Canada","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"","keywords":"Computer science; MEDLINE; Information retrieval; Search engine indexing; Metadata; World Wide Web; Chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002264327,0.001684134,0.001267174,0.01335229,0.001267191,0.004029045,0.001893476,0.001003189,0.3290959],"category_scores_gemma":[0.01587194,0.001222976,0.001338371,0.009605478,0.0004278402,0.004875424,0.002815739,0.001246474,0.1395296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001529763,"about_ca_system_score_gemma":0.003846253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00623475,"about_ca_topic_score_gemma":0.01110974,"domain_scores_codex":[0.9986693,0.0001891117,0.0003405146,0.0002543768,0.00045537,0.00009137701],"domain_scores_gemma":[0.9923397,0.002971387,0.0006689326,0.000904677,0.002768434,0.0003467295],"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":[0.0003717911,0.0000440574,0.001339735,0.001625465,0.00007861118,0.0001856149,0.0001161409,0.0001969427,0.002179829,0.006254404,0.8997042,0.08790325],"study_design_scores_gemma":[0.0001861288,0.00007371286,0.005516713,0.0006838175,0.000143185,0.0008664869,0.000219036,0.003891198,0.007616381,0.008046133,0.9726491,0.0001081463],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.005305951,0.002663744,0.06278487,0.001919794,0.0006105772,0.001358055,0.5603593,0.2626967,0.102301],"genre_scores_gemma":[0.02456836,0.002839293,0.2655697,0.001823187,0.0004339413,0.002619115,0.5702395,0.05074959,0.08115741],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.9977357,"threshold_uncertainty_score":0.9569632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005900698087821761,"score_gpt":0.2607618474742022,"score_spread":0.2548611493863804,"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."}}