{"id":"W6929093930","doi":"10.48448/mb1x-ax52","title":"Exploiting Hierarchically Structured Categories in Fine-grained Chinese Named Entity Recognition","year":2022,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"Research on Leishmaniasis Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Relevance (law); Function (biology); Named-entity recognition; Entity linking; Named entity","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":[],"consensus_categories":[],"category_scores_codex":[0.002397777,0.001343818,0.001094867,0.006271361,0.001148118,0.001262961,0.001881667,0.001350692,0.003318541],"category_scores_gemma":[0.005173524,0.0003008882,0.001110585,0.004677756,0.0006415399,0.004695535,0.002224899,0.001301737,0.00261588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001165364,"about_ca_system_score_gemma":0.001920059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02310096,"about_ca_topic_score_gemma":0.0454063,"domain_scores_codex":[0.9980087,0.0003781428,0.0002059916,0.0008490311,0.000344182,0.0002139937],"domain_scores_gemma":[0.9964282,0.0009499884,0.0003386693,0.001347995,0.0007542598,0.0001809445],"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":[0.0006689919,0.0004542704,0.0343232,0.001186444,0.0002489626,0.001413999,0.0009285618,0.02477317,0.02970359,0.01533257,0.07055444,0.8204118],"study_design_scores_gemma":[0.0001619606,0.0005209725,0.04599861,0.0002811482,0.0003411302,0.001812185,0.001573608,0.7091585,0.05312507,0.0468965,0.1398031,0.0003271989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3295247,0.009717705,0.5240947,0.002174733,0.0008441708,0.001855436,0.06176895,0.03998884,0.03003082],"genre_scores_gemma":[0.5870292,0.0012476,0.2987416,0.0008081329,0.0001839013,0.0004486802,0.1010532,0.0003731621,0.0101144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02310096,"threshold_uncertainty_score":0.04593301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03079762817752399,"score_gpt":0.32522183703327,"score_spread":0.2944242088557459,"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."}}