{"id":"W6964061517","doi":"10.25345/c5gh9bm8h","title":"MassIVE MSV000095292 - GNPS_Comprehensive Metabolite Profiling","year":2024,"lang":"en","type":"dataset","venue":"UC San Diego","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Metabolite; Profiling (computer programming); Monoclonal antibody","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","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.0004351985,0.001628509,0.001793987,0.001380451,0.0002400014,0.0005854808,0.00171335,0.0009122929,0.004948892],"category_scores_gemma":[0.0005063622,0.001525806,0.0007986756,0.00138493,0.0003600897,0.000303207,0.001444579,0.002598922,0.360862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004351232,"about_ca_system_score_gemma":0.0005667254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001527688,"about_ca_topic_score_gemma":0.0002411453,"domain_scores_codex":[0.9932466,0.000437561,0.001131702,0.002214465,0.001440873,0.001528862],"domain_scores_gemma":[0.9950506,0.000388511,0.0006705321,0.002903464,0.000488031,0.0004988521],"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":[0.00008727948,0.0001191529,0.000007670127,0.0008747487,0.001131253,0.001529177,0.00007178264,0.00003306655,0.0007768422,0.0003223376,0.994828,0.0002187286],"study_design_scores_gemma":[0.0004863432,0.00008137504,0.00003172179,0.0005566613,0.001929917,0.00007484456,0.0001910329,0.00007334626,0.001487602,0.001338086,0.9921739,0.001575163],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001454192,0.01534495,0.000002965931,0.00005311804,0.004153271,0.001528888,0.9760016,0.0008655888,0.00190426],"genre_scores_gemma":[0.00004492404,0.0002564921,0.0007115051,0.0004699053,0.002285189,0.0003813817,0.9940826,0.0005712038,0.001196763],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3559131,"threshold_uncertainty_score":0.9997021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02360059437423832,"score_gpt":0.2921498945105866,"score_spread":0.2685493001363483,"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."}}