{"id":"W2607162877","doi":"10.1007/978-1-4939-6899-2_15","title":"Determining the Localization of Carbohydrate Active Enzymes Within Gram-Negative Bacteria","year":2017,"lang":"en","type":"article","venue":"Methods in molecular biology","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Alberta; University of Lethbridge","funders":"Alberta Innovates","keywords":"Periplasmic space; Cytoplasm; Signal peptide; Bacterial outer membrane; Subcellular localization; Biochemistry; Biology; Enzyme; Bacteria; Gram-negative bacteria; Lysis; Extracellular; Protein subcellular localization prediction; Secretory protein; Peptide sequence; Secretion; Escherichia coli; Gene; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002402655,0.0003077935,0.0002675359,0.0004852636,0.0003306362,0.0007446436,0.0003280959,0.0003493347,0.0004379385],"category_scores_gemma":[0.0004984537,0.0002761987,0.0001685344,0.0004957847,0.0003785995,0.0004611034,0.0003288689,0.0005870179,0.0005249692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004479958,"about_ca_system_score_gemma":0.0003284781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002205993,"about_ca_topic_score_gemma":0.002392428,"domain_scores_codex":[0.9998438,0.00002973547,0.00001603111,0.00003484923,0.00004127566,0.00003439937],"domain_scores_gemma":[0.9997364,0.00008546744,0.00004106182,0.000036661,0.00004765975,0.00005270817],"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":[0.00008729874,0.000009738205,0.0003171858,0.00002058556,0.000001598783,0.0000235883,0.00004069028,0.00002681051,0.9979572,0.0001392558,0.0000117527,0.001364304],"study_design_scores_gemma":[0.000007885337,0.000098243,0.008496298,0.000008276776,0.00001404818,0.0001623087,0.0001959201,0.0006155379,0.988442,0.0002060329,0.001747233,0.000006248335],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.986003,0.001516998,0.0103377,0.0001191306,0.000018926,0.00002828474,0.0003745189,0.00005871408,0.001542681],"genre_scores_gemma":[0.9801587,0.001597159,0.01411176,0.0000546524,0.00001577181,0.00003457435,0.001111179,0.00005601577,0.002860268],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002205993,"threshold_uncertainty_score":0.004386246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03146437465139904,"score_gpt":0.4140591108266797,"score_spread":0.3825947361752806,"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."}}