{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002737511,0.00360965,0.001532591,0.004413024,0.00145875,0.003295945,0.002952231,0.00141043,0.02548088],"category_scores_gemma":[0.003283384,0.002036637,0.002657132,0.0025018,0.0004681843,0.002786402,0.004231368,0.003315673,0.02543496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009068906,"about_ca_system_score_gemma":0.002308414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003459811,"about_ca_topic_score_gemma":0.004336759,"domain_scores_codex":[0.9984885,0.0001811021,0.0001569585,0.0005150776,0.0004521976,0.0002061182],"domain_scores_gemma":[0.9985836,0.0003069611,0.0001213307,0.0003608036,0.0004269817,0.0002003398],"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":[0.002600566,0.0004148966,0.006742389,0.003828927,0.0007255363,0.001505961,0.001460249,0.008304445,0.193881,0.01046801,0.4290488,0.3410192],"study_design_scores_gemma":[0.0005141614,0.000291421,0.01234983,0.0006455415,0.0003888848,0.00170011,0.0004573848,0.06942943,0.1321405,0.02459728,0.7568774,0.0006082344],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007076269,0.00137968,0.4682083,0.0005807597,0.0004405369,0.0006523103,0.08715657,0.425006,0.009499619],"genre_scores_gemma":[0.03167868,0.001154307,0.6508139,0.0009482266,0.0001720727,0.001714684,0.2525653,0.05205296,0.008899889],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02548088,"threshold_uncertainty_score":0.08524197,"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."}}