{"id":"W4235401656","doi":"10.1515/iupac.86.0011","title":"Immunodiagnostics and Immunosensor Design","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Computer science; Data science; Nanotechnology; Biochemical engineering; Computational biology; Risk analysis (engineering); Medicine; Biology; Engineering; Materials science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002113949,0.0003319715,0.000286932,0.00005715921,0.000130555,0.00003484918,0.0002457568,0.00043053,0.00004269979],"category_scores_gemma":[0.0004122663,0.0002664991,0.00008060431,0.00005892623,0.0002237511,0.000002313669,0.0002581776,0.0001945302,5.855879e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004707434,"about_ca_system_score_gemma":0.0002151717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001460093,"about_ca_topic_score_gemma":0.00002245773,"domain_scores_codex":[0.9986748,0.00005451645,0.0002822086,0.0004870108,0.000223873,0.0002775698],"domain_scores_gemma":[0.9985456,0.00005238479,0.0001786236,0.0008601958,0.0002787406,0.00008447209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008798161,0.00006512812,9.342964e-7,0.00002028749,0.00005009376,0.000007585514,8.497836e-7,0.000001002618,0.01720892,0.000007663493,0.9781755,0.004374124],"study_design_scores_gemma":[0.0003565303,0.000327503,0.000009971585,0.0001176998,0.00005912638,0.00004396356,0.000004563542,0.000001797204,0.01096651,0.0002593785,0.9874863,0.0003666683],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001829424,0.002164853,0.019704,0.0002925253,0.0001126144,0.0003572998,0.9771441,0.00003486824,0.000006768918],"genre_scores_gemma":[0.0001007031,0.01630795,0.005257562,0.0002611821,0.0004524925,0.00002524217,0.9773335,0.00004248304,0.0002188214],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01444644,"threshold_uncertainty_score":0.9999787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01275986365168881,"score_gpt":0.3873172781935199,"score_spread":0.3745574145418311,"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."}}