{"id":"W3136631288","doi":"10.1101/2021.03.19.434579","title":"Analyzing Assay Specificity in Metabolomics using Unique Ion Signature Simulations","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Metabolomics; Fragment (logic); Resolution (logic); Chemistry; Identification (biology); Pairwise comparison; Computer science; Computational biology; Chromatography; Algorithm; Biology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001481473,0.000897215,0.0006704454,0.0005497296,0.0004615146,0.0007717675,0.00108355,0.001242041,0.002195448],"category_scores_gemma":[0.00534207,0.0003659832,0.0008045151,0.0004768978,0.0006537907,0.0008915119,0.00090978,0.001036497,0.0002833601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00102229,"about_ca_system_score_gemma":0.001294133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005857744,"about_ca_topic_score_gemma":0.002898039,"domain_scores_codex":[0.9996479,0.0001081838,0.00001816728,0.00006679709,0.0001006209,0.00005851694],"domain_scores_gemma":[0.9975209,0.00184153,0.0001817292,0.0001435255,0.0002212335,0.00009121799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001287222,0.00007398951,0.002563553,0.000113031,0.0000520405,0.00008985002,0.00004332426,0.9769757,0.008457543,0.007704868,0.000369436,0.003428006],"study_design_scores_gemma":[0.00001232721,0.00002460888,0.0002411734,0.000004983355,0.000007679493,0.00001105275,0.0000068096,0.9953092,0.002709194,0.001412353,0.0002551611,0.0000054059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6915922,0.0007228836,0.2891939,0.0007262944,0.0001266525,0.0002516295,0.001636734,0.00107318,0.01467659],"genre_scores_gemma":[0.9386852,0.0003226174,0.05796732,0.000207464,0.00001936295,0.0002980643,0.0006572339,0.0001629533,0.00167982],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005857744,"threshold_uncertainty_score":0.01164728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0166430236835706,"score_gpt":0.2448754347017078,"score_spread":0.2282324110181372,"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."}}