{"id":"W2008751190","doi":"10.4155/bio.09.138","title":"Computational Strategies for Metabolite Identification in Metabolomics","year":2009,"lang":"en","type":"review","venue":"Bioanalysis","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; National Institute for Nanotechnology","funders":"Canadian Institutes of Health Research","keywords":"Metabolomics; Identification (biology); Metabolite; Computational biology; Metabolite profiling; Computer science; Data science; Biology; Bioinformatics; Biochemistry; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005162574,0.0003929872,0.001524514,0.0005255121,0.00007929868,0.0001077246,0.0003302844,0.000292929,0.000007171798],"category_scores_gemma":[0.0001065483,0.0003424741,0.0009684931,0.0006457024,0.00005629429,0.000006868565,0.0000687308,0.0001116432,0.00000759171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003713406,"about_ca_system_score_gemma":0.000257705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001129812,"about_ca_topic_score_gemma":0.00003954485,"domain_scores_codex":[0.9978943,0.0001230714,0.0008498396,0.0006840637,0.0001568529,0.0002917973],"domain_scores_gemma":[0.9988474,0.00004553136,0.0004827217,0.0004285497,0.000144444,0.00005133669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001521868,0.0001250024,0.0000084187,0.001266006,0.002223614,0.000001232856,0.00001447002,0.0002963426,0.0001137308,0.00993384,0.0003508611,0.9856513],"study_design_scores_gemma":[0.0002012441,0.00003240573,0.00003976671,0.00007111394,0.002323899,0.000002342947,0.00004014027,0.00009783804,0.00001818062,0.001810579,0.9950105,0.0003520235],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002363163,0.9909492,0.007832447,0.00002640104,0.0001311022,0.0005121437,0.0003280277,0.000009690426,0.000187384],"genre_scores_gemma":[0.0003028972,0.9903346,0.005174546,0.00003841271,0.000225877,0.0002074681,0.003236512,0.000033679,0.0004459529],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9946596,"threshold_uncertainty_score":0.9999027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03263139242311345,"score_gpt":0.3423896221457493,"score_spread":0.3097582297226358,"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."}}