{"id":"W2319708779","doi":"10.1021/ac5039994","title":"Counting Missing Values in a Metabolite-Intensity Data Set for Measuring the Analytical Performance of a Metabolomics Platform","year":2014,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canadian Institutes of Health Research; Alberta Innovates - Health Solutions; Genome Canada","keywords":"Metabolomics; Chemistry; Missing data; Metabolite; Set (abstract data type); Data set; Intensity (physics); Biological system; Analytical Chemistry (journal); Chromatography; Statistics; Computer science; Mathematics; Physics; Optics","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":[],"consensus_categories":[],"category_scores_codex":[0.001608508,0.0002238368,0.0005336539,0.00003809088,0.0001239876,0.00003658204,0.0006384284,0.0001485211,0.00000841012],"category_scores_gemma":[0.001850912,0.0001682742,0.000153299,0.0002133582,0.0002337905,0.00001379252,0.0004903024,0.0002041226,8.429349e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001820567,"about_ca_system_score_gemma":0.00007166889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002119402,"about_ca_topic_score_gemma":0.000006134788,"domain_scores_codex":[0.9982893,0.0000252368,0.0004980175,0.0005344729,0.000232444,0.0004204985],"domain_scores_gemma":[0.9985125,0.0001406622,0.0001623686,0.0009053286,0.0001951277,0.00008403153],"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":[0.001443525,0.0004088518,0.09614253,0.001172922,0.001805308,0.000003545735,0.0001751628,0.0004520596,0.885314,0.003421103,0.002628211,0.007032712],"study_design_scores_gemma":[0.00153524,0.0001222801,0.01010066,0.00006577511,0.0006846978,0.00002711251,0.0003510856,0.4321458,0.5254826,0.0008409473,0.02803361,0.0006102283],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948649,0.0008421305,0.002086635,0.0003194246,0.00005407929,0.0001338737,0.00007175243,0.000008452835,0.001618766],"genre_scores_gemma":[0.9974893,0.0002460094,0.001428269,0.0001483885,0.0002901674,0.000008519092,0.0001765887,0.0000205078,0.0001922142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4316937,"threshold_uncertainty_score":0.6862023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05044087893627682,"score_gpt":0.2942048540020695,"score_spread":0.2437639750657927,"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."}}