{"id":"W4391397032","doi":"10.3389/fmicb.2024.1340413","title":"CyanoCyc cyanobacterial web portal","year":2024,"lang":"en","type":"article","venue":"Frontiers in Microbiology","topic":"Algal biology and biofuel production","field":"Energy","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Mount Allison University","funders":"Lawrence Livermore National Laboratory; Biological and Environmental Research; U.S. Department of Energy; Natural Environment Research Council; Sight Research UK; Biotechnology and Biological Sciences Research Council; Directorate for Biological Sciences","keywords":"UniProt; Genome; Ontology; Computer science; Computational biology; Visualization; Informatics; Function (biology); Data science; Suite; Biology; Gene; Data mining; Geography; Genetics","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":[0.001025734,0.001366437,0.001102957,0.004616726,0.001543155,0.004045191,0.002414617,0.001194694,0.09568719],"category_scores_gemma":[0.002999498,0.0005638038,0.000570606,0.006246295,0.0004318511,0.002681212,0.005083419,0.001425006,0.06163404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00187089,"about_ca_system_score_gemma":0.003748557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01143313,"about_ca_topic_score_gemma":0.01349736,"domain_scores_codex":[0.999323,0.00007060626,0.00004379997,0.0001034929,0.0003596109,0.00009937555],"domain_scores_gemma":[0.9986539,0.0002012901,0.00009777198,0.0002416167,0.0004311274,0.0003743139],"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":[0.0005723176,0.00008388893,0.002217992,0.001620693,0.0000529467,0.0005297322,0.0002129327,0.0006570539,0.0132311,0.0100629,0.9089956,0.06176271],"study_design_scores_gemma":[0.0001032247,0.00002709483,0.003028831,0.0002154802,0.00002504753,0.0002524881,0.0001105746,0.002259427,0.006694567,0.005110664,0.9821004,0.00007224019],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.009646473,0.003944966,0.03006707,0.001959227,0.0006587178,0.0005865323,0.5998496,0.161855,0.1914324],"genre_scores_gemma":[0.03630242,0.003714774,0.04272496,0.002100177,0.000313242,0.0008052539,0.8479549,0.0220387,0.04404571],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.09568719,"threshold_uncertainty_score":0.3201055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005975549972290603,"score_gpt":0.2109879077850055,"score_spread":0.2050123578127149,"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."}}