{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000428609,0.000145719,0.0003056525,0.0001527415,0.0001271485,0.00007065284,0.0003092448,0.00008837956,0.001094462],"category_scores_gemma":[0.0001903775,0.0001111876,0.0001091601,0.0002514427,0.0002153825,0.000009677399,0.0011745,0.0002160611,0.00004542857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001030875,"about_ca_system_score_gemma":0.0001497971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001323742,"about_ca_topic_score_gemma":0.00005589492,"domain_scores_codex":[0.9980856,0.0003416225,0.0002619231,0.0005119632,0.0002815956,0.0005173111],"domain_scores_gemma":[0.998363,0.00002674805,0.00003544319,0.0007317536,0.0007200791,0.0001230314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004287697,0.0001055754,0.001801529,0.00003716093,0.0002778706,0.00001094107,0.00007734873,1.180214e-7,0.9947054,0.001064772,0.001029012,0.0008473583],"study_design_scores_gemma":[0.0003933277,0.00005896368,0.002276071,0.000005600791,0.00001136907,0.00000303041,0.0002243807,0.00000172062,0.7667108,0.0007837141,0.2294279,0.0001032008],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9867184,0.008856923,0.0001414783,0.0005559821,0.0001483175,0.0001259545,0.0006835199,0.000006485222,0.002762915],"genre_scores_gemma":[0.9920594,0.002151021,0.002087807,0.00009006461,0.0002314598,0.00002863104,0.00238255,0.0000235575,0.0009454712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2283988,"threshold_uncertainty_score":0.9998187,"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."}}