{"id":"W3101141081","doi":"10.1101/2020.11.20.391300","title":"2019 Association of Biomolecular Resource Facilities Multi-Laboratory Data-Independent Acquisition Study","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bioinformatics Solutions (Canada)","funders":"National Institute of Standards and Technology; National Cancer Institute; Instituto Tecnológico de Costa Rica; National Science Foundation","keywords":"Computer science; Sample (material); Data acquisition; Software; Set (abstract data type); Data set; Resource (disambiguation); Data mining; Instrumentation (computer programming); Data science; Database; Information retrieval; Operating system; Chemistry; Artificial intelligence; Chromatography","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":[0.07441265,0.001316185,0.001954724,0.002958247,0.002599437,0.006031246,0.003591764,0.002395163,0.05376191],"category_scores_gemma":[0.07934331,0.001121988,0.001345336,0.005451348,0.001846215,0.002582026,0.007387067,0.002790304,0.03141396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003717445,"about_ca_system_score_gemma":0.01243624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005319464,"about_ca_topic_score_gemma":0.004889898,"domain_scores_codex":[0.9514642,0.01480786,0.007293813,0.009646454,0.01331153,0.003476216],"domain_scores_gemma":[0.8297167,0.026898,0.01344824,0.06706971,0.05321033,0.009657098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.01752607,0.002679924,0.1803548,0.002475095,0.00182696,0.001182202,0.001077329,0.001868162,0.01953421,0.01339994,0.4653945,0.2926807],"study_design_scores_gemma":[0.001798908,0.001954742,0.14158,0.000832433,0.0004764826,0.00169957,0.00058411,0.006581778,0.02542565,0.006892597,0.8119367,0.0002369108],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2571254,0.004777716,0.2238727,0.02025599,0.009484833,0.01942202,0.3139643,0.02016683,0.1309302],"genre_scores_gemma":[0.4503169,0.001095095,0.163046,0.01051414,0.002264273,0.03058437,0.2646242,0.008887718,0.06866737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07441265,"threshold_uncertainty_score":0.3935364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02331250223180772,"score_gpt":0.2642105296910446,"score_spread":0.2408980274592369,"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."}}