{"id":"W7002085825","doi":"","title":"Literature Mining in Molecular Biology","year":2002,"lang":"en","type":"article","venue":"NPARC","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biomedical text mining; Reading (process); Domain (mathematical analysis); Process (computing); MEDLINE; Information extraction","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008048507,0.001529593,0.002716301,0.02489823,0.002472677,0.0086131,0.003743279,0.002501448,0.01581355],"category_scores_gemma":[0.02391357,0.001053626,0.002414776,0.02830098,0.00269503,0.009063629,0.004251011,0.002836495,0.01343534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001880357,"about_ca_system_score_gemma":0.004518698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001302784,"about_ca_topic_score_gemma":0.001457384,"domain_scores_codex":[0.9905638,0.003276288,0.00169826,0.001820275,0.002400824,0.0002406258],"domain_scores_gemma":[0.9806141,0.01311442,0.001854326,0.002045745,0.001865161,0.000506349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001398043,0.0001781882,0.001730064,0.01388732,0.0005100925,0.001078765,0.00109358,0.00307315,0.004030034,0.1493285,0.07667759,0.7482729],"study_design_scores_gemma":[0.00005859619,0.00008642457,0.002348832,0.004837641,0.0002078555,0.001890802,0.0005556055,0.004716773,0.003226191,0.2201148,0.7618663,0.00009003229],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007710865,0.1974002,0.6805971,0.01449413,0.004460851,0.00238707,0.01653784,0.008903245,0.06750871],"genre_scores_gemma":[0.03828737,0.1118452,0.7974522,0.005113349,0.003202329,0.002360536,0.02275572,0.0007202834,0.01826295],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02489823,"threshold_uncertainty_score":0.05290163,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01469741343752993,"score_gpt":0.2649923506805892,"score_spread":0.2502949372430592,"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."}}