{"id":"W2773610445","doi":"10.1186/s40168-017-0375-2","title":"MetaLab: an automated pipeline for metaproteomic data analysis","year":2017,"lang":"en","type":"article","venue":"Microbiome","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":187,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Genomics; Genome Canada","keywords":"Metaproteomics; Metagenomics; Profiling (computer programming); Biology; Computational biology; Identification (biology); Pipeline (software); Computer science; Software; Bioinformatics; Data mining; Ecology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002229147,0.0001721631,0.0003102713,0.00009188989,0.0004895476,0.000179469,0.001787171,0.0001135075,0.0002566546],"category_scores_gemma":[0.00004827494,0.0001654487,0.0001319822,0.000132161,0.00009832679,0.0003523923,0.0004003173,0.00009120165,0.00002230017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000423675,"about_ca_system_score_gemma":0.00003068738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001249312,"about_ca_topic_score_gemma":0.00005728247,"domain_scores_codex":[0.9988202,0.000005979369,0.0002737064,0.0005981263,0.00005428334,0.0002476589],"domain_scores_gemma":[0.995717,0.00001719543,0.0002991833,0.003811669,0.00007887912,0.00007600208],"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":[0.00001273489,0.00006805761,0.00006308239,0.00002409142,0.0002428513,6.865507e-7,0.000007733365,0.00001294444,0.995407,0.0002024281,0.00213565,0.001822697],"study_design_scores_gemma":[0.0003865897,0.000007330918,0.00004066733,0.000006023866,0.0008319791,0.000004057441,0.000007937745,0.08536246,0.8439925,0.0009161913,0.06816407,0.0002801559],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2067269,0.0001792001,0.7760613,0.001253317,0.00005507622,0.0009045774,0.01089846,0.002146174,0.001775005],"genre_scores_gemma":[0.4919289,0.000021041,0.497146,0.00008033936,0.0001224086,0.0002746284,0.006664949,0.00005099983,0.003710757],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.285202,"threshold_uncertainty_score":0.6746804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05355176978710362,"score_gpt":0.383271716526838,"score_spread":0.3297199467397344,"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."}}