{"id":"W2020060326","doi":"10.1118/1.3469557","title":"TH‐D‐201C‐09: Evaluation of Metabolomics Data Using Univariate and Multivariate Statistical Analysis Techniques","year":2010,"lang":"en","type":"article","venue":"Medical Physics","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Bonferroni correction; Univariate; Multivariate statistics; Metabolite; Population; Multivariate analysis; Metabolomics; Receiver operating characteristic; Principal component analysis; Linear discriminant analysis; Nuclear medicine; Partial least squares regression; Univariate analysis; Mathematics; Medicine; Internal medicine; Statistics; Biology; Bioinformatics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004242075,0.0008941725,0.001131872,0.00274646,0.0004341321,0.001048463,0.0005676592,0.0004623023,0.003628058],"category_scores_gemma":[0.005684904,0.0002217982,0.0009282261,0.002053289,0.0007518596,0.0006913082,0.0007034192,0.001063247,0.0007131976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006448313,"about_ca_system_score_gemma":0.001035759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001103586,"about_ca_topic_score_gemma":0.002024423,"domain_scores_codex":[0.9979814,0.0005775607,0.0001724064,0.0004093955,0.0007398561,0.0001194898],"domain_scores_gemma":[0.9954103,0.002021498,0.0009415348,0.0005330506,0.0008594411,0.0002341355],"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":[0.008343682,0.001755529,0.103462,0.00144295,0.00161196,0.0007170847,0.000418366,0.01593241,0.4418171,0.003315947,0.01099983,0.4101832],"study_design_scores_gemma":[0.0002316754,0.007240364,0.3626859,0.00007441284,0.0004402128,0.002018603,0.0002562836,0.2993111,0.314954,0.001886322,0.01054009,0.0003610065],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4717132,0.0006252378,0.4983408,0.0003034843,0.0001491231,0.00115203,0.01212681,0.01118228,0.004406967],"genre_scores_gemma":[0.5779209,0.0003819009,0.4054386,0.0001545011,0.00007082346,0.003303648,0.008172745,0.001267448,0.003289444],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004242075,"threshold_uncertainty_score":0.02243453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04633919074405689,"score_gpt":0.3621498403067662,"score_spread":0.3158106495627093,"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."}}