{"id":"W2162473677","doi":"10.1093/bioinformatics/btv361","title":"MetaPathways v2.5: quantitative functional, taxonomic and usability improvements","year":2015,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia; University of British Columbia","funders":"Pacific Northwest National Laboratory; Natural Sciences and Engineering Research Council of Canada; Genome Alberta; University of British Columbia; Genome British Columbia; Genome Canada; Tula Foundation; Canadian Institute for Advanced Research; Western Canada Research Grid; Compute Canada","keywords":"Computer science; Usability; Pipeline (software); Annotation; Software; Data mining; Information retrieval; Artificial intelligence; Human–computer interaction; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002391735,0.0001188707,0.000120896,0.00002267302,0.00006537543,0.0000252441,0.00007240461,0.00005844944,0.000003060923],"category_scores_gemma":[0.0001007999,0.0001036338,0.00003938261,0.00003371428,0.00009716531,0.000002647745,0.0001558394,0.00003488769,0.00001892749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001582336,"about_ca_system_score_gemma":0.00007224907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000146934,"about_ca_topic_score_gemma":0.00001456587,"domain_scores_codex":[0.9993942,0.00001494463,0.0002265631,0.0001317826,0.00008516635,0.0001473675],"domain_scores_gemma":[0.9995097,0.00001125628,0.00008889704,0.0001934909,0.00009939122,0.00009728154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001452622,0.0008347189,0.1430353,0.0006582336,0.002618554,0.000004684364,0.007918132,0.001461543,0.6546769,0.02393529,0.1029404,0.0604636],"study_design_scores_gemma":[0.01050568,0.006950853,0.123505,0.00004281801,0.0003850057,0.0001018153,0.02214857,0.03260223,0.1403442,0.008118086,0.6523921,0.002903596],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991015,0.0008242881,0.004329653,0.00005251028,0.0002341303,0.0002007355,0.00005869461,0.000004653869,0.003280324],"genre_scores_gemma":[0.9771309,0.0001360012,0.02222167,0.0002326318,0.00006276442,0.00002078756,0.00004303856,0.000008457166,0.0001437118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5494518,"threshold_uncertainty_score":0.4226064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05624295090273047,"score_gpt":0.2580242905753689,"score_spread":0.2017813396726384,"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."}}