{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001974337,0.003265375,0.001996081,0.004694549,0.0009764857,0.003407279,0.00235876,0.001532041,0.04618133],"category_scores_gemma":[0.008052099,0.0006243337,0.002506431,0.007777423,0.000415334,0.001545928,0.002108849,0.001841505,0.07379776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001646242,"about_ca_system_score_gemma":0.0035834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01021757,"about_ca_topic_score_gemma":0.02025701,"domain_scores_codex":[0.9973462,0.000412428,0.0004141919,0.000987592,0.0005720417,0.0002676237],"domain_scores_gemma":[0.9969341,0.000896128,0.0004937721,0.0007574627,0.0007489557,0.0001696191],"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":[0.0009346932,0.00008883901,0.00967652,0.01121785,0.0005003386,0.0001095196,0.00007763069,0.001340456,0.002237056,0.002776162,0.9272555,0.0437854],"study_design_scores_gemma":[0.0001716725,0.00004559178,0.007223209,0.0009484761,0.0001784659,0.0001263151,0.00006579106,0.0004707773,0.001463107,0.002296537,0.9869762,0.00003395916],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000579667,0.001138127,0.000545331,0.00008114641,0.00005083211,0.00003793421,0.9949239,0.0006254493,0.002017712],"genre_scores_gemma":[0.0008465437,0.00061988,0.0009981308,0.00006867936,0.000007477671,0.0001000725,0.9964527,0.00008776176,0.0008187372],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04618133,"threshold_uncertainty_score":0.1544919,"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."}}