{"id":"W2022787379","doi":"10.1021/ac062455y","title":"Top-Down Lipidomic Screens by Multivariate Analysis of High-Resolution Survey Mass Spectra","year":2007,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":185,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thermo Fisher Scientific (Canada)","funders":"Deutsche Forschungsgemeinschaft","keywords":"Lipidomics; Chemistry; Orbitrap; Mass spectrometry; Principal component analysis; Mass spectrum; Chromatography; Analytical Chemistry (journal); Resolution (logic); Computational biology; Biological system; Biochemistry; Artificial intelligence; Computer 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":[0.0006681962,0.001440705,0.0009972018,0.0007664578,0.0004109761,0.0007525223,0.0003710797,0.0002718639,0.001271118],"category_scores_gemma":[0.0009321199,0.0003144206,0.0006803799,0.0005824149,0.0003959289,0.0003137657,0.0008324962,0.0008786098,0.00059146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003049649,"about_ca_system_score_gemma":0.000488881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005631482,"about_ca_topic_score_gemma":0.001052697,"domain_scores_codex":[0.9994862,0.00009627228,0.00004181584,0.00009182423,0.0002144978,0.00006941339],"domain_scores_gemma":[0.9995431,0.0001704553,0.00007188262,0.00007733302,0.00008820497,0.0000491381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000687361,0.00003446608,0.0002153456,0.00002697666,0.00001268112,0.00003929976,0.000008394984,0.000394411,0.9950605,0.00007355554,0.00004407072,0.004021525],"study_design_scores_gemma":[0.000009890332,0.0001793625,0.001747573,0.000001808634,0.00002955618,0.0001048586,0.00001020279,0.005647627,0.9915271,0.0001229087,0.000603705,0.00001527946],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7425776,0.0004888471,0.2491636,0.0002506304,0.00004934664,0.0008773351,0.001754687,0.003166751,0.001671245],"genre_scores_gemma":[0.7715474,0.001473188,0.2204059,0.0002238985,0.00002388809,0.0008572069,0.002444065,0.0004520334,0.002572408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001440705,"threshold_uncertainty_score":0.004252374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01039801315330879,"score_gpt":0.2648314201146634,"score_spread":0.2544334069613546,"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."}}