{"id":"W6939599152","doi":"10.6084/m9.figshare.14700419.v1","title":"Additional file 1 of SMILE: systems metabolomics using interpretable learning and evolution","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Queen's University","funders":"","keywords":"Feature (linguistics); Key (lock); Pattern recognition (psychology); Interpretability; Feature selection","routes":{"ca_aff":true,"ca_fund":false,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002164017,0.002058231,0.001681311,0.00255667,0.001233226,0.002601585,0.003160407,0.001533849,0.819216],"category_scores_gemma":[0.01681556,0.001006113,0.001469512,0.004292708,0.0005148349,0.002948274,0.001948557,0.001777476,0.26124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001068308,"about_ca_system_score_gemma":0.001757295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004374369,"about_ca_topic_score_gemma":0.007646547,"domain_scores_codex":[0.9991611,0.0001640627,0.00007538115,0.0002585334,0.0002280553,0.0001128474],"domain_scores_gemma":[0.9892755,0.008081996,0.000366223,0.0008472512,0.001062184,0.0003668855],"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":[0.00038381,0.0001013199,0.001343528,0.003427861,0.00008857626,0.00008518246,0.00006607296,0.001879254,0.000766481,0.001871161,0.9802863,0.009700529],"study_design_scores_gemma":[0.002166463,0.0002572421,0.008591677,0.001415312,0.0002745519,0.0003599213,0.0002167638,0.00771205,0.003635947,0.02485962,0.950295,0.0002154584],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0001806969,0.00003731242,0.001704088,0.00008258739,0.00004557727,0.00005938503,0.9941729,0.002195197,0.001522349],"genre_scores_gemma":[0.006155447,0.000174746,0.01131527,0.0003770149,0.00008254371,0.0008307579,0.9693784,0.005637384,0.00604841],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.819216,"threshold_uncertainty_score":0.2578663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01393999674952811,"score_gpt":0.2332580437649233,"score_spread":0.2193180470153951,"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."}}