{"id":"W2981123106","doi":"10.3389/fgene.2019.00999","title":"An Integrated Pipeline for Annotation and Visualization of Metagenomic Contigs","year":2019,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":147,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"University of Calgary; Alberta Innovates; Genome Canada; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Government of Alberta","keywords":"Annotation; Metagenomics; Perl; Computer science; Gene Annotation; Genome; Pipeline (software); Contig; Visualization; Computational biology; Gene prediction; JavaScript; Genome project; Sequence assembly; Data mining; Biology; Gene; World Wide Web; Artificial intelligence; Genetics; Programming language; Transcriptome","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":[0.000135413,0.00009642733,0.0001643935,0.00006289952,0.00001734475,0.00000827941,0.00008059162,0.00008360935,0.000001588721],"category_scores_gemma":[0.00001861794,0.00009867291,0.00002886892,0.00006017862,0.00004233699,9.21514e-7,0.00002437931,0.00002020279,2.361226e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000077736,"about_ca_system_score_gemma":0.00002966229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006078463,"about_ca_topic_score_gemma":0.0000158405,"domain_scores_codex":[0.9993886,0.000031366,0.0002074091,0.000216328,0.00004282754,0.0001135007],"domain_scores_gemma":[0.9996168,0.00000551437,0.00008283111,0.0001639282,0.0001036889,0.00002726437],"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":[0.0001126677,0.00003764602,0.1511549,0.0000343749,0.00003944738,6.470651e-8,0.0001732419,0.001039195,0.8344485,0.00005475621,0.0004102162,0.012495],"study_design_scores_gemma":[0.004537939,0.001932376,0.1609877,0.00003188032,0.0001263225,0.000002882806,0.001761756,0.0988705,0.6512523,0.001017521,0.07879558,0.0006832512],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8759986,0.002494567,0.1207721,0.00001002635,0.0002714522,0.0003523904,0.00006538325,0.000001390075,0.00003413497],"genre_scores_gemma":[0.973364,0.0009627547,0.02513571,0.00004758787,0.00004244059,0.00001481575,0.0002737119,0.00001747745,0.0001414926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1831962,"threshold_uncertainty_score":0.4023765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007233862100876299,"score_gpt":0.2519627553992547,"score_spread":0.2447288932983784,"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."}}