{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003890017,0.00534151,0.00162897,0.00440738,0.001078266,0.002593772,0.003525817,0.001199976,0.009773183],"category_scores_gemma":[0.005492376,0.001818687,0.002295352,0.002505749,0.0007789952,0.002576989,0.004368915,0.002419314,0.009826454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008804414,"about_ca_system_score_gemma":0.002684123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002608898,"about_ca_topic_score_gemma":0.001867344,"domain_scores_codex":[0.9980868,0.0002780319,0.0002646065,0.0005534776,0.0006203278,0.0001967154],"domain_scores_gemma":[0.9979181,0.000594179,0.0002946712,0.000380735,0.0005885736,0.000223724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004748802,0.0005693564,0.0107825,0.004192908,0.002053387,0.001462227,0.001063521,0.01843638,0.2500437,0.005098558,0.1921371,0.5094115],"study_design_scores_gemma":[0.0009931038,0.0006599168,0.01189491,0.000351293,0.0003727204,0.001484332,0.0003699567,0.6042784,0.2263255,0.01897545,0.1336247,0.0006696515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01030151,0.0005551142,0.5924631,0.0002453765,0.0001378539,0.000567669,0.01424505,0.3804469,0.001037393],"genre_scores_gemma":[0.06488308,0.0004356212,0.8711823,0.0004586534,0.00009577128,0.002230583,0.04211285,0.01681129,0.001789921],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009773183,"threshold_uncertainty_score":0.03269458,"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."}}