{"id":"W4302810357","doi":"10.1145/3487553.3524875","title":"The International Workshop on Semantics-enabled Biomedical Literature Analytics (SeBiLAn)","year":2022,"lang":"en","type":"article","venue":"Companion Proceedings of the Web Conference 2022","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Citation; Commonwealth; Analytics; Library science; Computer science; World Wide Web; Data science; History; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01695342,0.001625348,0.002028995,0.009006816,0.001854967,0.01069715,0.00291797,0.002542019,0.05983293],"category_scores_gemma":[0.028457,0.0009959157,0.002962205,0.007753758,0.001961729,0.01573495,0.01262292,0.004018726,0.04117181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002330701,"about_ca_system_score_gemma":0.007988713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003652544,"about_ca_topic_score_gemma":0.00547936,"domain_scores_codex":[0.9895336,0.004219769,0.001084741,0.00172896,0.00287745,0.000555469],"domain_scores_gemma":[0.9814858,0.008175693,0.0004336332,0.004022478,0.003820167,0.002062317],"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":[0.0002498324,0.000276015,0.0007016459,0.001547694,0.0001729288,0.0002972781,0.0008747169,0.001508374,0.003425848,0.0507867,0.4846264,0.4555326],"study_design_scores_gemma":[0.00002915295,0.00004211739,0.0006680844,0.0008164168,0.00004133157,0.0002502886,0.0003690294,0.005878672,0.001357158,0.06381131,0.9267017,0.00003466485],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.008938194,0.07641731,0.6177657,0.07144359,0.02920613,0.001665727,0.03585255,0.02727211,0.1314387],"genre_scores_gemma":[0.04235183,0.04956493,0.5891148,0.01679012,0.0059775,0.001743898,0.1285831,0.007062052,0.1588119],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9909932,"threshold_uncertainty_score":0.2001611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01940018326463586,"score_gpt":0.2592494292537504,"score_spread":0.2398492459891146,"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."}}