{"id":"W2808973977","doi":"10.1093/bioinformatics/bty466","title":"iMetaLab 1.0: a web platform for metaproteomics data analysis","year":2018,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":50,"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","keywords":"Metaproteomics; Computer science; Pipeline (software); Task (project management); Data mining; Data science; Database; World Wide Web; Metagenomics; Engineering; Biology; Systems engineering","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":[0.0003980648,0.0001456734,0.0002183362,0.0001110186,0.0001489164,0.00004884081,0.0005060901,0.0001490677,0.00003083271],"category_scores_gemma":[0.00008945808,0.0001257772,0.0001257376,0.0002603535,0.00009152001,0.0000164669,0.0002865691,0.00004967882,0.00006161081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001336387,"about_ca_system_score_gemma":0.0001834568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007954386,"about_ca_topic_score_gemma":0.0001623852,"domain_scores_codex":[0.9990129,0.000007422587,0.0003704147,0.0002171395,0.00008626223,0.0003058152],"domain_scores_gemma":[0.9985326,0.00001036507,0.0001621262,0.001066939,0.0001330503,0.00009493809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000727037,0.000347123,0.003807781,0.0007939316,0.007006507,0.000001229245,0.001121306,0.00004748169,0.7142974,0.001864516,0.239783,0.03020269],"study_design_scores_gemma":[0.001701462,0.0008582392,0.001299112,0.00001219588,0.00134545,0.00001795897,0.0003456382,0.1219118,0.08155029,0.0001145052,0.7902139,0.0006294107],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6942586,0.0004307599,0.2940511,0.0003707368,0.0005869094,0.001530246,0.003355137,0.00007512121,0.005341399],"genre_scores_gemma":[0.5891561,0.0002571306,0.3976521,0.001710634,0.0008534014,0.00003445366,0.008889361,0.00004178024,0.001405054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6327471,"threshold_uncertainty_score":0.5129046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04160231172779637,"score_gpt":0.3111446754283295,"score_spread":0.2695423637005331,"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."}}