{"id":"W4402939787","doi":"10.1101/2024.09.27.615406","title":"MetaLab Platform Enables Comprehensive DDA and DIA Metaproteomics Analysis","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Metaproteomics; Computer science; Chemistry","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005475253,0.0007488013,0.001437165,0.002461148,0.0003677603,0.004461408,0.002151658,0.0003711462,0.0001262495],"category_scores_gemma":[0.001454203,0.0006200954,0.0005890682,0.004882307,0.0003296472,0.0003142161,0.00725718,0.000910853,0.0005015319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001855991,"about_ca_system_score_gemma":0.0003490591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001694848,"about_ca_topic_score_gemma":0.00002375124,"domain_scores_codex":[0.9922844,0.0002545983,0.001406065,0.003281526,0.00205196,0.0007214803],"domain_scores_gemma":[0.992783,0.0007828139,0.000753482,0.004219033,0.001017316,0.0004443835],"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":[0.000441112,0.001771679,0.04496169,0.004791735,0.06005573,0.002586638,0.0009979445,0.02948733,0.6088986,0.08747545,0.1568683,0.001663827],"study_design_scores_gemma":[0.002041132,0.0002208839,0.3129068,0.001071113,0.02095075,1.528957e-7,0.0004392609,0.1907381,0.1233469,0.004344881,0.3361285,0.007811596],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782743,0.00481443,0.01040023,0.0004913161,0.003748855,0.0008483603,0.0008527607,0.0004901784,0.00007958591],"genre_scores_gemma":[0.9867298,0.0001770093,0.01231584,0.0002042464,0.0002775955,0.00008475378,0.000001638429,0.0000693857,0.0001397587],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4855517,"threshold_uncertainty_score":0.999625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07225809982584294,"score_gpt":0.3036627112244575,"score_spread":0.2314046113986146,"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."}}