{"id":"W2771234895","doi":"10.22541/au.151326160.02842606","title":"Empowering users - self-service metabolomics data analysis for everyone?","year":2017,"lang":"en","type":"dataset","venue":"Authorea","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Metabolomics; Software; World Wide Web; Data science; Bioinformatics; Biology","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":[0.0448737,0.003141366,0.002724314,0.002857436,0.002347353,0.01346956,0.007251736,0.006778991,0.0735992],"category_scores_gemma":[0.1639664,0.001978553,0.002591264,0.003065053,0.003335096,0.036191,0.02122386,0.01007462,0.1292308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009109922,"about_ca_system_score_gemma":0.004400205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009132283,"about_ca_topic_score_gemma":0.001382612,"domain_scores_codex":[0.9748464,0.01167133,0.001831757,0.003181528,0.006800956,0.001668068],"domain_scores_gemma":[0.8253362,0.05577071,0.005481087,0.05562198,0.03486652,0.02292353],"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":[0.0005089677,0.0002179039,0.004383617,0.0007253733,0.00009531779,0.0005747809,0.00356248,0.0001782237,0.00275315,0.005509949,0.7468008,0.2346895],"study_design_scores_gemma":[0.0001378698,0.000164425,0.001861585,0.001190878,0.00007059884,0.00142089,0.002477436,0.002214478,0.003391931,0.03989718,0.9469162,0.0002565769],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.01011298,0.009613148,0.3577812,0.2996659,0.01600861,0.001037493,0.008091962,0.2454083,0.05228046],"genre_scores_gemma":[0.09831446,0.01172283,0.5142052,0.1649892,0.01857686,0.002849675,0.01896869,0.09368511,0.07668807],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.0735992,"threshold_uncertainty_score":0.2462139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04543022776042047,"score_gpt":0.3556454194444384,"score_spread":0.310215191684018,"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."}}