{"id":"W4242147305","doi":"10.1515/iupac.88.0265","title":"Enzymatic Decomposition","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Enzyme Catalysis and Immobilization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Extraction (chemistry); Computer science; Decomposition; Process engineering; Sample preparation; Sample (material); Scale (ratio); Throughput; Chromatography; Biochemical engineering; Biological system; Chemistry; Engineering; Organic chemistry; Physics","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":[0.0002953839,0.000295207,0.0003406617,0.00007967072,0.000186004,0.000102906,0.000439741,0.0004190331,0.0002928812],"category_scores_gemma":[0.0003832428,0.0002765516,0.0001826665,0.00004375369,0.00009015561,0.000005457132,0.0001775873,0.000175392,0.000003322536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006480651,"about_ca_system_score_gemma":0.000316915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003121926,"about_ca_topic_score_gemma":0.0005050711,"domain_scores_codex":[0.9985523,0.0000580911,0.0003191506,0.0004606127,0.000386018,0.0002238457],"domain_scores_gemma":[0.9979787,0.000007297963,0.0003435152,0.001237959,0.0003432485,0.0000892797],"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":[0.00005434818,0.0001262424,0.00000547623,0.00009821777,0.00009659337,0.000005473012,0.000002185215,0.000010989,0.004999962,9.194774e-7,0.9941257,0.0004739298],"study_design_scores_gemma":[0.0004130532,0.0002500714,0.00004063914,0.000114887,0.0001419235,0.00001432511,0.000006652298,0.00001305507,0.005807327,0.00001474505,0.9928705,0.0003128551],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002045479,0.001473242,0.0005033077,0.00006947645,0.0003898689,0.0002003928,0.9952686,0.00001079798,0.0000388131],"genre_scores_gemma":[0.0005341283,0.001267512,0.0000647807,0.0001813862,0.0008471733,0.0000179525,0.9965885,0.00003053638,0.0004680701],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.00151135,"threshold_uncertainty_score":0.9999686,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01068939945542505,"score_gpt":0.4080159094515645,"score_spread":0.3973265099961394,"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."}}