{"id":"W2791530712","doi":"10.4155/bio-2018-0020","title":"Bioanalytical Techniques in Lipidomics","year":2018,"lang":"en","type":"article","venue":"Bioanalysis","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Lipidomics; Bioanalysis; Biochemical engineering; Chemistry; Nanotechnology; Computational biology; Data science; Chromatography; Computer science; Biology; Materials science; Engineering; Biochemistry","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":[0.0002656041,0.0001312507,0.0002337002,0.0002292814,0.00005407103,0.00001992271,0.0001771689,0.0001100012,0.00008448936],"category_scores_gemma":[0.0001377629,0.0001146874,0.0001417518,0.0005042345,0.0001855728,0.000002033184,0.0001355928,0.00005593555,0.00002778947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001880562,"about_ca_system_score_gemma":0.00002628794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006040225,"about_ca_topic_score_gemma":0.0004365334,"domain_scores_codex":[0.999046,0.00003842761,0.0002306024,0.0003445809,0.0001016289,0.0002387359],"domain_scores_gemma":[0.9994427,0.00000851581,0.00005442837,0.0003542574,0.00008546538,0.00005463548],"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":[0.0002616524,0.0004527817,0.130562,0.00002277618,0.001420808,0.00001325182,0.00009736523,0.000002845367,0.7662703,0.006394217,0.006718373,0.08778372],"study_design_scores_gemma":[0.0004249318,0.0003548235,0.008702314,0.000007226863,0.0001676415,0.000004734182,0.0001150398,0.0002990488,0.4170706,0.0005272899,0.5719119,0.000414421],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9553612,0.005004698,0.00465159,0.001216108,0.0002862182,0.0002297987,0.00002597828,0.00005163452,0.03317273],"genre_scores_gemma":[0.9920491,0.001153611,0.005083193,0.0002701363,0.0005115064,0.00001223158,0.00002026143,0.00001281769,0.000887159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5651935,"threshold_uncertainty_score":0.4676816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008776781583058089,"score_gpt":0.2676986165530379,"score_spread":0.2589218349699798,"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."}}