{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008317145,0.0002320951,0.0005333435,0.00009580575,0.00006434333,0.00001654815,0.0002437574,0.0002442564,0.0002115934],"category_scores_gemma":[0.000451482,0.0002210796,0.0003011981,0.0007406566,0.0001669685,0.000003447231,0.000112193,0.0001541874,0.000004780976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003671559,"about_ca_system_score_gemma":0.00003717658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006037336,"about_ca_topic_score_gemma":0.0001399492,"domain_scores_codex":[0.9982647,0.00004248981,0.0004860805,0.000524686,0.000237492,0.0004445864],"domain_scores_gemma":[0.9989058,0.0000889594,0.0001739993,0.0005009125,0.0001640675,0.0001662487],"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":[0.0002110212,0.0001236007,0.02218795,0.00001753503,0.002033391,0.00000246714,0.000004795261,0.00009080103,0.9725181,0.0002020881,0.002488328,0.0001198546],"study_design_scores_gemma":[0.0007333317,0.00007896899,0.119422,0.000004761961,0.001013184,0.000001604731,0.00004204066,0.001803515,0.8734553,0.0000736183,0.003006096,0.0003655201],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9628943,0.0005552829,0.03238844,0.00008331198,0.00005671678,0.00007064548,0.0003374788,0.00001265872,0.003601226],"genre_scores_gemma":[0.9956512,0.0002040429,0.001154516,0.00006238701,0.0001765087,0.00000240816,0.001028711,0.00001767254,0.00170255],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09906282,"threshold_uncertainty_score":0.9015365,"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."}}