{"id":"W4393735954","doi":"10.5281/zenodo.7644150","title":"Interaction Metabolomics to Uncover Synergists in Natural Product Mixtures - DATASETS","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Metabolomics; Product (mathematics); Natural product; Natural (archaeology); Computer science; Chemistry; Data mining; Chromatography; Mathematics; Biology; Stereochemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.001578061,0.003589986,0.002094876,0.003417685,0.0009318605,0.002166465,0.003729251,0.00411617,0.01245443],"category_scores_gemma":[0.003596761,0.0006051326,0.002920971,0.003938053,0.0006514256,0.0009531107,0.002346733,0.002235704,0.01012418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001411768,"about_ca_system_score_gemma":0.001954541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007213264,"about_ca_topic_score_gemma":0.0157183,"domain_scores_codex":[0.9988966,0.0002062699,0.0001086397,0.0003721347,0.0002888143,0.000127601],"domain_scores_gemma":[0.9988064,0.0004231763,0.0001788649,0.0002823741,0.0001502423,0.0001589926],"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":[0.003097874,0.001239633,0.01910051,0.01553951,0.002498152,0.001414709,0.00016525,0.01951964,0.01247299,0.005320981,0.8854955,0.03413525],"study_design_scores_gemma":[0.001813625,0.0004492176,0.02450861,0.0008117998,0.0008847078,0.001027309,0.0001480018,0.01386149,0.00855348,0.007600288,0.940165,0.0001764464],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00422931,0.001038084,0.0006965289,0.000174725,0.00004707694,0.00006905695,0.9917988,0.001092612,0.0008538671],"genre_scores_gemma":[0.003790471,0.00027841,0.002428642,0.0001041426,0.000008818528,0.0001758402,0.9927756,0.00005828905,0.000379861],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01245443,"threshold_uncertainty_score":0.04166418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02075646166631013,"score_gpt":0.2775289480439071,"score_spread":0.256772486377597,"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."}}