{"id":"W3134813985","doi":"10.1016/j.watres.2021.117017","title":"CyanoMetDB, a comprehensive public database of secondary metabolites from cyanobacteria","year":2021,"lang":"en","type":"article","venue":"Water Research","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":334,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"NordForsk; Novo Nordisk; Jane ja Aatos Erkon Säätiö; Universidade de São Paulo; Santen; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Novo Nordisk Fonden; Conselho Nacional de Desenvolvimento Científico e Tecnológico; European Commission; Fundação de Amparo à Pesquisa do Estado de São Paulo; Marie Curie","keywords":"Cyanobacteria; Secondary metabolite; Biology; Metadata; World Wide Web; Computer science; Biochemistry","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":[0.0008864681,0.002109004,0.001962808,0.01054825,0.001088799,0.002192562,0.001263806,0.001361032,0.007434299],"category_scores_gemma":[0.002604984,0.0005140631,0.001011601,0.010717,0.0003801586,0.001775671,0.002135481,0.0009920322,0.006266267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001142334,"about_ca_system_score_gemma":0.004441655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004870716,"about_ca_topic_score_gemma":0.006884834,"domain_scores_codex":[0.9990829,0.0001017623,0.0001868764,0.0002375286,0.000303374,0.00008752233],"domain_scores_gemma":[0.9988154,0.0002536562,0.0002732918,0.0001079107,0.000292663,0.0002571497],"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":[0.005213981,0.0006052216,0.03463064,0.0614997,0.001411147,0.004547291,0.001129317,0.005191302,0.3203038,0.007517759,0.382729,0.1752209],"study_design_scores_gemma":[0.0004276359,0.0003856388,0.05117211,0.001523759,0.0008625674,0.001704369,0.0004210109,0.002879599,0.03687985,0.002429616,0.9011213,0.0001924907],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01750902,0.01108365,0.002935462,0.0001669858,0.00008296525,0.0002160159,0.9588776,0.003512725,0.005615685],"genre_scores_gemma":[0.01271944,0.004564632,0.007612739,0.0001303504,0.00003532433,0.0002248534,0.973617,0.00023963,0.00085602],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01054825,"threshold_uncertainty_score":0.02487022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07150262671724915,"score_gpt":0.3340669411610941,"score_spread":0.262564314443845,"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."}}