{"id":"W3123863472","doi":"10.1021/acs.analchem.0c05022","title":"DaDIA: Hybridizing Data-Dependent and Data-Independent Acquisition Modes for Generating High-Quality Metabolomic Data","year":2021,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Canada Foundation for Innovation","keywords":"Metabolomics; Workflow; Data acquisition; Metabolome; Data quality; Computer science; Chemistry; Data mining; Database; Chromatography","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":[0.001096506,0.0002693891,0.0004579174,0.00001842367,0.0002099973,0.000152076,0.001150885,0.0001755287,0.00005836865],"category_scores_gemma":[0.000841168,0.0002667067,0.00005554132,0.00008903437,0.0001025707,0.00003803704,0.004526045,0.000173213,0.000002371847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001973154,"about_ca_system_score_gemma":0.0001561128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003815141,"about_ca_topic_score_gemma":0.00005396764,"domain_scores_codex":[0.9971147,0.00006452545,0.0004360572,0.001715182,0.0002662669,0.0004033396],"domain_scores_gemma":[0.9960908,0.00008740266,0.0001492255,0.003355536,0.0001512146,0.0001658581],"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":[0.0000551057,0.00009100288,0.0002312314,0.0001219075,0.0005238606,0.00001288755,0.00000305361,0.0000231099,0.9903168,0.0005395096,0.007101688,0.0009798205],"study_design_scores_gemma":[0.001971529,0.00004563569,0.0008696248,0.00002022787,0.001001119,0.00008563738,0.0004056756,0.03696271,0.921384,0.0005103093,0.03586853,0.0008749685],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.956898,0.009412188,0.02404661,0.001154098,0.0001241441,0.0001887138,0.007529567,0.00002705379,0.0006196646],"genre_scores_gemma":[0.9508657,0.001351874,0.01223129,0.0003557919,0.0009344941,0.00001208266,0.03337629,0.00003340434,0.0008390063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06893279,"threshold_uncertainty_score":0.9999785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07971584717098781,"score_gpt":0.3585413851942196,"score_spread":0.2788255380232317,"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."}}