{"id":"W3216359166","doi":"10.1101/2021.11.24.469852","title":"A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sciex (Canada); Spinal Cord Injury BC","funders":"Vlaamse regering; Fonds Wetenschappelijk Onderzoek; Agence Nationale de la Recherche","keywords":"Benchmark (surveying); Data acquisition; Computer science; Data mining; Proteomics; Proteome; Data science; Bioinformatics; Biology; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.006929051,0.003479426,0.001678247,0.004781774,0.002308224,0.002954094,0.004194041,0.003092004,0.005625219],"category_scores_gemma":[0.00943801,0.000746725,0.001987853,0.008522872,0.001213556,0.002503572,0.003548098,0.002303104,0.01021124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002301884,"about_ca_system_score_gemma":0.003249489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009222951,"about_ca_topic_score_gemma":0.01359138,"domain_scores_codex":[0.9919267,0.001244022,0.0008618857,0.002425013,0.00286422,0.0006782144],"domain_scores_gemma":[0.9937417,0.001507987,0.000381429,0.002043826,0.001995394,0.0003296098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001513958,0.0009336523,0.01486669,0.003858273,0.0007244502,0.0006800415,0.0002173116,0.0189587,0.02758579,0.00342468,0.8439957,0.08324073],"study_design_scores_gemma":[0.0009066636,0.0008236598,0.07363083,0.0007019225,0.0003359168,0.002167942,0.0004519543,0.06394873,0.0587157,0.01427723,0.7836108,0.000428628],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.05329128,0.005240969,0.02135472,0.001012159,0.0004716487,0.0005483452,0.8900748,0.02007681,0.007929306],"genre_scores_gemma":[0.01091958,0.0004817966,0.01487389,0.0002334449,0.00003490539,0.0003936162,0.9717577,0.0005993831,0.000705813],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.009222951,"threshold_uncertainty_score":0.03664476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01651257485733412,"score_gpt":0.257694095054305,"score_spread":0.2411815201969709,"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."}}