{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005100825,0.002098724,0.0009736565,0.002968903,0.0006229714,0.002278948,0.002285724,0.001100577,0.002788075],"category_scores_gemma":[0.00431348,0.001257718,0.001627677,0.001927876,0.000810514,0.002281317,0.002778777,0.0023249,0.001767806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005868946,"about_ca_system_score_gemma":0.001340034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000822297,"about_ca_topic_score_gemma":0.001087078,"domain_scores_codex":[0.9983199,0.0002807169,0.0001850359,0.000609728,0.0004779908,0.0001266216],"domain_scores_gemma":[0.9977254,0.0007893925,0.0002852106,0.0004682963,0.0005782411,0.0001534077],"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":[0.001901269,0.0003947852,0.01030758,0.001381531,0.0006191715,0.0006093954,0.000434829,0.008569126,0.7465274,0.00494988,0.01288044,0.2114245],"study_design_scores_gemma":[0.0002761411,0.0004001685,0.004843533,0.00008383948,0.0001642274,0.000931851,0.0001028201,0.1763387,0.7753886,0.004876733,0.03624464,0.0003487693],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0339323,0.0007906696,0.9270134,0.0003269455,0.0001412463,0.0003674382,0.003461033,0.03249125,0.001475728],"genre_scores_gemma":[0.06914109,0.0004106106,0.922015,0.0004202174,0.00005707225,0.0006164785,0.004654054,0.001705057,0.0009804029],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005100825,"threshold_uncertainty_score":0.02697611,"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."}}