{"id":"W6926326751","doi":"10.25345/c5571801n","title":"MassIVE MSV000094296 - Comprehensive Untargeted Lipidomic Profiling of Third Generation Lentiviral Vectors and Packaging Cells: Raw LCMS Data","year":2024,"lang":"en","type":"dataset","venue":"UC San Diego","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Third generation; Profiling (computer programming); Drug discovery; Real world data","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002761514,0.0003451422,0.0006207633,0.0003297222,0.0001328988,0.00007099853,0.0002550764,0.0003425743,0.0001930521],"category_scores_gemma":[0.0001892361,0.0003154063,0.00008391119,0.0002614317,0.0001751577,0.0001764303,0.0002577131,0.0006899434,0.000321035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001327293,"about_ca_system_score_gemma":0.0004661145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001366258,"about_ca_topic_score_gemma":0.0008476637,"domain_scores_codex":[0.99764,0.0001304921,0.0007777973,0.0007671081,0.0003420102,0.0003426143],"domain_scores_gemma":[0.9980525,0.0002261165,0.0003300754,0.001006933,0.0002379882,0.000146326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008197895,0.00007740728,0.0004119797,0.001677315,0.0001487471,0.00002627966,0.0004100046,0.00002052528,0.009058589,0.000008526166,0.9853626,0.002716008],"study_design_scores_gemma":[0.0003440962,0.0008277928,0.0003678994,0.003253366,0.002154669,0.00005176693,0.007144933,0.01145968,0.2080486,0.0003616273,0.7648664,0.001119173],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1476401,0.005467303,0.00007040591,0.0004615136,0.006321909,0.001434853,0.8384884,0.0000740511,0.00004142072],"genre_scores_gemma":[0.04923066,0.001261111,0.0004274526,0.0002998593,0.002453438,0.00003027459,0.9461872,0.00004383401,0.00006616268],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2204963,"threshold_uncertainty_score":0.9999298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1881363688823987,"score_gpt":0.4068416424254856,"score_spread":0.2187052735430869,"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."}}