{"id":"W2946048361","doi":"10.1016/j.dib.2019.104037","title":"Determination of the degree of PEGylation of protein bioconjugates using data from proton nuclear magnetic resonance spectroscopy","year":2019,"lang":"en","type":"article","venue":"Data in Brief","topic":"Chemical Synthesis and Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ontario Institute of Technology; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research","keywords":"PEGylation; Nuclear magnetic resonance spectroscopy; Nuclear magnetic resonance; Chemistry; Proton magnetic resonance; Spectroscopy; Biochemistry; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001549328,0.00005882389,0.0001335736,0.00001666171,0.000009027706,0.0000050206,0.0006415556,0.00005811144,0.00005202328],"category_scores_gemma":[0.0001660579,0.00004667749,0.000024117,0.00009719667,0.0000659523,0.00001190882,0.000507572,0.00003455237,6.844511e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005104657,"about_ca_system_score_gemma":0.00002295378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006649269,"about_ca_topic_score_gemma":0.00007975913,"domain_scores_codex":[0.9992836,0.00003956156,0.0002406749,0.0002591111,0.0001109859,0.00006610109],"domain_scores_gemma":[0.9985023,0.00001297157,0.0001848466,0.001260294,0.00003040899,0.000009187921],"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":[0.00003746762,0.0000427482,0.01779805,0.00004484073,0.000004570059,5.880492e-8,0.000007208213,0.000001155227,0.9783431,0.000006375854,0.00001344973,0.003700984],"study_design_scores_gemma":[0.0001904928,0.00003984868,0.01584892,0.0001173013,0.00001884556,2.116716e-7,0.00001187936,0.008728043,0.9731829,0.0000543931,0.001753728,0.00005348659],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980893,0.0004916376,0.0001048198,0.00002542609,0.000008855832,0.0003274356,0.0008994313,9.211325e-7,0.00005223165],"genre_scores_gemma":[0.9936204,0.00003866376,0.005526895,0.0000072856,0.00002053331,0.000002431762,0.0007660712,0.000007392635,0.00001032948],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008726888,"threshold_uncertainty_score":0.1903453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04099418051333508,"score_gpt":0.2752444589075216,"score_spread":0.2342502783941866,"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."}}