{"id":"W3028550569","doi":"10.1021/cen-09407-scicon002","title":"2-D NMR Assesses Biosimilar Structure","year":2016,"lang":"en","type":"article","venue":"C&EN Global Enterprise","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biosimilar; Food and drug administration; Filgrastim; Similarity (geometry); Agency (philosophy); Computer science; Medicine; Pharmacology; Artificial intelligence; Internal medicine; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001131015,0.0004201526,0.0003480638,0.001486719,0.0003985625,0.0007449765,0.0003334946,0.001425635,0.003344952],"category_scores_gemma":[0.00180264,0.0003385693,0.0003257087,0.0006498896,0.0005719747,0.0009103362,0.0005343151,0.0007917676,0.0009109401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003670652,"about_ca_system_score_gemma":0.0002652455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008002197,"about_ca_topic_score_gemma":0.001128128,"domain_scores_codex":[0.9994439,0.0001315144,0.00002520941,0.0001291734,0.0001999886,0.0000700475],"domain_scores_gemma":[0.998772,0.0004116662,0.0002408821,0.0001449042,0.0003214879,0.0001091656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002567876,0.00009483155,0.003952817,0.00009320662,0.00005439126,0.00008597584,0.0001226711,0.0007679056,0.9864329,0.0003991941,0.0003818801,0.007357514],"study_design_scores_gemma":[0.0001155157,0.001605793,0.08490617,0.0000393387,0.0002154127,0.001473229,0.0008151837,0.01872991,0.8788521,0.001773806,0.01130941,0.0001640672],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9349713,0.0019259,0.04428393,0.0004898179,0.0001417561,0.0001047084,0.001228409,0.0005360786,0.0163181],"genre_scores_gemma":[0.9589378,0.001221762,0.03467409,0.0006498587,0.0000637354,0.0000944166,0.001082501,0.0001123448,0.003163491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003344952,"threshold_uncertainty_score":0.01119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007380757636083193,"score_gpt":0.2784915947939825,"score_spread":0.2711108371578993,"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."}}