{"id":"W4290033717","doi":"10.1101/2022.08.03.502704","title":"Molecular formula discovery via bottom-up MS/MS interrogation","year":2022,"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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Interrogation; Annotation; Computer science; Ranking (information retrieval); Metabolomics; False discovery rate; Tandem mass spectrometry; Computational biology; Data mining; Mass spectrometry; Information retrieval; Chemistry; Chromatography; Artificial intelligence; Biology; Biochemistry; Gene","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":[0.002419684,0.002534703,0.0016444,0.004867071,0.0009321776,0.002980849,0.001691815,0.001041206,0.005680954],"category_scores_gemma":[0.005076558,0.0006847715,0.001711746,0.002051146,0.0006693096,0.001784723,0.002497724,0.001706738,0.005077558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007243085,"about_ca_system_score_gemma":0.001059115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001440893,"about_ca_topic_score_gemma":0.002861013,"domain_scores_codex":[0.9983824,0.0002192928,0.00008416912,0.0005551503,0.0006057771,0.0001531169],"domain_scores_gemma":[0.996361,0.001255515,0.0005637414,0.000806601,0.0008570969,0.0001561581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001400002,0.0002394108,0.02467948,0.001598813,0.0006285569,0.0009705055,0.0002339222,0.01037074,0.6276232,0.003326698,0.01476161,0.3141671],"study_design_scores_gemma":[0.0001047068,0.0004016322,0.01577958,0.0001586395,0.0005784215,0.00161113,0.0002202029,0.2577392,0.6660732,0.01896253,0.03812551,0.0002450894],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2176217,0.005767725,0.7080622,0.0012031,0.0003435455,0.0004460924,0.01758657,0.03986197,0.009107126],"genre_scores_gemma":[0.3694554,0.001597576,0.6023104,0.0009415139,0.0001863691,0.0002455874,0.01754942,0.002751268,0.004962383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005680954,"threshold_uncertainty_score":0.01900464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008762002030758494,"score_gpt":0.2251396219465348,"score_spread":0.2163776199157763,"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."}}