{"id":"W1901898645","doi":"10.5489/cuaj.795","title":"Quantifying CUA’s progress","year":2013,"lang":"en","type":"article","venue":"Canadian Urological Association Journal","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Urological Association","funders":"","keywords":"Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.007297519,0.0006288469,0.0007661547,0.01254539,0.002190101,0.00573335,0.001059508,0.00127124,0.003834762],"category_scores_gemma":[0.04380547,0.0002719144,0.0009088927,0.01032765,0.0008481656,0.006111935,0.002749966,0.001227483,0.001726555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00321226,"about_ca_system_score_gemma":0.004872073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05380596,"about_ca_topic_score_gemma":0.04656913,"domain_scores_codex":[0.9940925,0.001124787,0.0005219671,0.001530648,0.002085252,0.0006447913],"domain_scores_gemma":[0.9644766,0.01402572,0.003277541,0.003619642,0.01279734,0.001802997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000605772,0.0002547968,0.4648163,0.0007087783,0.0004234894,0.0004370508,0.00437296,0.02085227,0.005876228,0.03225559,0.02825628,0.4411406],"study_design_scores_gemma":[0.00004547022,0.0002923011,0.3996655,0.0005174872,0.0007164825,0.001217099,0.0129732,0.285356,0.02439583,0.04926403,0.2252516,0.0003050878],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7772651,0.00786928,0.07822464,0.006916509,0.001415865,0.0003895955,0.02770963,0.006953847,0.09325553],"genre_scores_gemma":[0.9462225,0.0009362168,0.03698897,0.0001807621,0.0001477449,0.0001196257,0.007923303,0.0003464595,0.007134435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05380596,"threshold_uncertainty_score":0.1069855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02086673815076005,"score_gpt":0.2555186236013835,"score_spread":0.2346518854506235,"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."}}