{"id":"W4225545880","doi":"10.1038/s41597-022-01216-6","title":"A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics","year":2022,"lang":"en","type":"article","venue":"Scientific Data","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Spinal Cord Injury BC","funders":"Vlaamse regering; Fonds Wetenschappelijk Onderzoek; Agence Nationale de la Recherche","keywords":"Computer science; Data acquisition; Benchmark (surveying); Data mining; Proteomics; Data science; Biology","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":[0.006867699,0.003377895,0.001691936,0.004998242,0.002260172,0.002949395,0.004268678,0.002971747,0.006534886],"category_scores_gemma":[0.009823054,0.0007219848,0.001943162,0.00891515,0.001163651,0.002561006,0.003570923,0.002169908,0.01119644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002345872,"about_ca_system_score_gemma":0.003333165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009731618,"about_ca_topic_score_gemma":0.01526651,"domain_scores_codex":[0.9921926,0.00117705,0.0008423346,0.002388904,0.002744941,0.0006542508],"domain_scores_gemma":[0.9937057,0.001513859,0.0004332257,0.001918172,0.002092189,0.0003368263],"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":[0.001484255,0.0008480361,0.01566339,0.004057079,0.0007307357,0.0006275797,0.0002078085,0.01520214,0.02576451,0.003014123,0.8525802,0.07982001],"study_design_scores_gemma":[0.0008400079,0.0007742568,0.07588575,0.0007554712,0.0003257762,0.001914246,0.0004400397,0.05147869,0.05028442,0.01224441,0.804642,0.0004149875],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04297774,0.004818044,0.01636336,0.0008848231,0.0003974882,0.0004822864,0.9100295,0.01710822,0.006938575],"genre_scores_gemma":[0.009684305,0.0004406646,0.01227173,0.0002270364,0.00003119901,0.0003705327,0.9758479,0.0005019419,0.0006246247],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.009731618,"threshold_uncertainty_score":0.03632027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04527096868765498,"score_gpt":0.3220505935548303,"score_spread":0.2767796248671754,"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."}}