{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009084975,0.004228597,0.002466561,0.004524841,0.001424895,0.002658688,0.003878026,0.003311236,0.08456169],"category_scores_gemma":[0.003706467,0.001032414,0.002424388,0.00601614,0.0005734154,0.001187204,0.002768569,0.001617278,0.09511692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001350848,"about_ca_system_score_gemma":0.002731984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02232709,"about_ca_topic_score_gemma":0.04182652,"domain_scores_codex":[0.9990228,0.0001180176,0.0000664718,0.0003486799,0.0002479727,0.0001960724],"domain_scores_gemma":[0.9987506,0.0003529763,0.0001089598,0.0003221479,0.0002805385,0.0001848196],"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":[0.0002672811,0.00003669569,0.001354265,0.001004233,0.0001265908,0.0000658248,0.00003003086,0.0008256059,0.001064813,0.0005054359,0.9908444,0.003874925],"study_design_scores_gemma":[0.0009006824,0.00008159137,0.007563679,0.0004665243,0.0002588218,0.0001994104,0.00009521085,0.002772301,0.003177496,0.004262108,0.9801084,0.0001137185],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004181696,0.0002092444,0.0001707575,0.00006427481,0.00004016473,0.00001503171,0.996111,0.001599964,0.001371409],"genre_scores_gemma":[0.0007762169,0.00009053543,0.0005995851,0.00007405554,0.000008759723,0.00005061476,0.9975458,0.0001957584,0.0006586762],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08456169,"threshold_uncertainty_score":0.282887,"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."}}