{"id":"W3017288814","doi":"10.1101/2020.04.16.038703","title":"Comprehensive database of secondary metabolites from cyanobacteria","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Algal biology and biofuel production","field":"Energy","cited_by":40,"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; Fundação de Amparo à Pesquisa do Estado de São Paulo; Conselho Nacional de Desenvolvimento Científico e Tecnológico; European Commission","keywords":"Cyanobacteria; Metadata; Identification (biology); Computer science; Database; Biology; Ecology; World Wide Web","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000201061,0.0006387518,0.0009995649,0.0001605108,0.0001129613,0.00006611538,0.0006893353,0.0007663458,0.0006800072],"category_scores_gemma":[0.0002169679,0.0006410446,0.0002345051,0.0003456403,0.0003063116,0.0002076182,0.0009526296,0.001098248,0.0002388833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007803687,"about_ca_system_score_gemma":0.0004236486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008553587,"about_ca_topic_score_gemma":0.000006879984,"domain_scores_codex":[0.9968835,0.0003325932,0.0007505004,0.001348867,0.0002594755,0.0004250901],"domain_scores_gemma":[0.9969167,0.0001106463,0.0006810291,0.001526336,0.0005351767,0.0002301147],"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":[0.0001624753,0.00009475156,0.001932886,0.0004935447,0.0005766627,0.00003099836,0.00001215005,0.00001137087,0.9945378,0.001767891,0.0003727386,0.000006723715],"study_design_scores_gemma":[0.0004335117,0.0000444059,0.08863679,0.0001647831,0.0002575298,9.991514e-9,0.00000508444,0.00003299627,0.8701447,0.00004603129,0.03960879,0.0006254022],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856444,0.005797792,0.0002410271,0.0003818053,0.002843575,0.0004253747,0.004215211,0.0004108141,0.00004000623],"genre_scores_gemma":[0.9896521,0.0007025611,0.00751825,0.000438286,0.001492677,0.00006082577,0.00003320188,0.00009932477,0.000002787993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1243931,"threshold_uncertainty_score":0.9996041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0225156315091124,"score_gpt":0.2245028715270581,"score_spread":0.2019872400179457,"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."}}