{"id":"W3169612605","doi":"10.3390/separations8060084","title":"Automated Screening and Filtering Scripts for GC×GC-TOFMS Metabolomics Data","year":2021,"lang":"en","type":"article","venue":"Separations","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Genome Alberta; Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Foundation for Innovation; Genome Canada","keywords":"Mass spectrometry; Chemistry; Metabolomics; Gas chromatography; Mass spectrum; Chromatography; Mass; Resolution (logic); Scripting language; Electron ionization; Gas chromatography–mass spectrometry; Analytical Chemistry (journal); Ionization; Ion; Computer science; Organic chemistry; Artificial intelligence","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":[0.0001480616,0.0001162012,0.0001629157,0.00003397385,0.0002362629,0.00007069144,0.0001439944,0.0000662852,0.00001206014],"category_scores_gemma":[0.0002279481,0.000119058,0.00004270377,0.0001056803,0.00004072478,0.00001096262,0.0003523963,0.0000439756,0.000001866903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003432444,"about_ca_system_score_gemma":0.0000564062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007330002,"about_ca_topic_score_gemma":0.000120022,"domain_scores_codex":[0.999159,0.00002655581,0.0001701637,0.00039645,0.00005738997,0.0001904121],"domain_scores_gemma":[0.9992815,0.00001929761,0.00005256083,0.00048858,0.0001057294,0.00005230273],"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":[0.00002028172,0.00003295076,0.0002780494,0.00001738552,0.0002457996,0.000001810948,0.00003921575,0.00008712192,0.9653679,0.002155295,0.03077822,0.0009760278],"study_design_scores_gemma":[0.001052686,0.0001050887,0.003260034,0.00001207601,0.0001910429,0.00005042102,0.000282017,0.02706603,0.2275896,0.0003385946,0.7396589,0.0003934642],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7535625,0.02144467,0.2155097,0.002391515,0.0009163414,0.0006941789,0.002210833,0.0001798062,0.003090453],"genre_scores_gemma":[0.8264367,0.002369617,0.1617145,0.0006092547,0.0004572195,0.00007828622,0.005058113,0.0000417616,0.003234542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7377782,"threshold_uncertainty_score":0.4855044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0577787569908453,"score_gpt":0.3337905097954403,"score_spread":0.276011752804595,"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."}}