{"id":"W2041921251","doi":"10.1371/journal.pone.0052038","title":"Semi-Automatic In Silico Gap Closure Enabled De Novo Assembly of Two Dehalobacter Genomes from Metagenomic Data","year":2012,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Strategic Environmental Research and Development Program; University of Toronto; Ontario Genomics Institute; Government of Canada; Government of Ontario; Genome Canada; Ontario Genomics; Joint Genome Institute; U.S. Department of Defense","keywords":"Contig; Metagenomics; Sequence assembly; Genome; In silico; Computational biology; Biology; Bacterial genome size; Subspecies; Shotgun sequencing; Whole genome sequencing; Genetics; Gene; Ecology","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":[0.001399882,0.0008983792,0.001094906,0.0007725573,0.0005497852,0.001103753,0.0009012888,0.0007120282,0.0009760825],"category_scores_gemma":[0.003428818,0.0007082836,0.001027235,0.0007832607,0.0002570729,0.0006725538,0.001482402,0.001408563,0.0007490955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000371349,"about_ca_system_score_gemma":0.0006893208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007273271,"about_ca_topic_score_gemma":0.00135296,"domain_scores_codex":[0.9991845,0.0001963422,0.0001146401,0.0002601721,0.0001605896,0.000083732],"domain_scores_gemma":[0.9982962,0.000813212,0.0002066623,0.0003396604,0.0002315059,0.0001127792],"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":[0.0005834257,0.0001432199,0.002409938,0.0003639191,0.00007512222,0.0004346424,0.0006786597,0.01092609,0.9398701,0.0009638436,0.0003253793,0.04322571],"study_design_scores_gemma":[0.0000769952,0.0003175872,0.007513111,0.00004389281,0.0001309914,0.0004970675,0.0002321471,0.1635879,0.8166683,0.001288692,0.009543627,0.00009968966],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5376225,0.000497489,0.4548178,0.0001718437,0.00009089182,0.0002568588,0.001659238,0.003737536,0.001145928],"genre_scores_gemma":[0.3800306,0.0002586334,0.6117173,0.00006582952,0.00001659093,0.0002657855,0.006055804,0.0006917028,0.0008977442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001399882,"threshold_uncertainty_score":0.007403314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06202097705849022,"score_gpt":0.2647020497644613,"score_spread":0.202681072705971,"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."}}