{"id":"W2902584424","doi":"10.4155/bio-2018-0268","title":"2018 White Paper on Recent Issues in Bioanalysis: <i>‘A Global Bioanalytical Community Perspective on Last Decade of Incurred Samples Reanalysis (ISR)’</i> (Part 1 – Small Molecule Regulated Bioanalysis, Small Molecule Biomarkers, Peptides &amp; Oligonucleotide Bioanalysis)","year":2018,"lang":"en","type":"article","venue":"Bioanalysis","topic":"Biosimilars and Bioanalytical Methods","field":"Immunology and Microbiology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"","keywords":"Bioanalysis; Biopharmaceutical; Nanotechnology; Computer science; Chemistry; Biology; Biotechnology; Chromatography","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","sts","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.005058753,0.002167115,0.004845301,0.004237615,0.001117945,0.0002896879,0.00285491,0.001782412,0.003387726],"category_scores_gemma":[0.003121557,0.001831557,0.003570724,0.01630583,0.004598702,0.000289123,0.001004697,0.00185797,0.0006899601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001615469,"about_ca_system_score_gemma":0.0003530801,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01606748,"about_ca_topic_score_gemma":0.05699152,"domain_scores_codex":[0.9825965,0.006747922,0.004067236,0.003220828,0.0009492975,0.002418245],"domain_scores_gemma":[0.9888609,0.001315119,0.001987115,0.004920449,0.002299237,0.0006171907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.02278766,0.04096214,0.2667345,0.0008514168,0.3282568,0.0004296425,0.006008905,0.001853546,0.1857744,0.01609161,0.03958981,0.09065957],"study_design_scores_gemma":[0.02156406,0.009487756,0.1006561,0.003319697,0.1300104,0.0001788556,0.03692621,0.006133919,0.3457382,0.005904136,0.3233385,0.01674213],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8316222,0.07115004,0.004174818,0.03968062,0.001952906,0.00368942,0.007269247,0.0015403,0.0389205],"genre_scores_gemma":[0.9792643,0.006260161,0.007428359,0.002603423,0.00028163,0.00008524401,0.00148318,0.0001883574,0.002405409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2837487,"threshold_uncertainty_score":0.9995135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04670819109761333,"score_gpt":0.3150279030080699,"score_spread":0.2683197119104566,"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."}}