{"id":"W6960372239","doi":"10.1371/journal.pone.0124844.g001","title":"Flow chart representing patient selection in the metabolomics sub-study.","year":2015,"lang":"en","type":"other","venue":"Figshare","topic":"Wheat and Barley Genetics and Pathology","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Flow chart; Selection (genetic algorithm); Chart; Feature selection; Metabolomics; Control chart","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001280711,0.00107723,0.001242238,0.001343281,0.001496192,0.001030742,0.0007283197,0.0007700631,0.09916402],"category_scores_gemma":[0.0035621,0.0004647703,0.0005101028,0.001957349,0.0003183602,0.0007117626,0.0007059338,0.00134142,0.01522895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001311197,"about_ca_system_score_gemma":0.003090767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01398846,"about_ca_topic_score_gemma":0.01674393,"domain_scores_codex":[0.999299,0.000144045,0.0001291587,0.0002219542,0.00007991928,0.0001258581],"domain_scores_gemma":[0.998292,0.0003757822,0.0002418733,0.000182866,0.0005209994,0.0003865513],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01405544,0.003252451,0.1770359,0.00165978,0.0003848836,0.001667288,0.0004664551,0.001434648,0.004073213,0.003105632,0.7077121,0.08515223],"study_design_scores_gemma":[0.01527695,0.006467008,0.4369595,0.002964892,0.0006161958,0.007160756,0.002052612,0.006831003,0.003135278,0.008451759,0.5096721,0.000411896],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.1059415,0.0040778,0.01198117,0.002864805,0.001527143,0.03637761,0.7770037,0.001424055,0.05880215],"genre_scores_gemma":[0.2941104,0.005619993,0.02449936,0.009287707,0.001102757,0.06587606,0.5463588,0.0005403901,0.0526045],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.09916402,"threshold_uncertainty_score":0.3317367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03942743157719923,"score_gpt":0.2355792290713716,"score_spread":0.1961517974941724,"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."}}