{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003264858,0.00018225,0.0003584786,0.00004632498,0.00003341832,0.00001318331,0.0004412506,0.0001087251,0.00004204143],"category_scores_gemma":[0.00007937967,0.0001783619,0.00005547192,0.00007342217,0.00004670068,0.00000357674,0.0004363746,0.00008216747,0.00001621845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002399863,"about_ca_system_score_gemma":0.00006946472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001515462,"about_ca_topic_score_gemma":0.0002163826,"domain_scores_codex":[0.9987448,0.00009340651,0.0003234545,0.000321955,0.0001257212,0.0003906039],"domain_scores_gemma":[0.9988598,0.00004561446,0.0001256114,0.0008305715,0.00004721919,0.00009117608],"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":[0.00001563052,0.0004108921,0.2835504,0.00003822958,0.0004637029,0.000001028531,0.0001806391,0.000008200436,0.7150892,0.00001162159,0.00004033583,0.0001901973],"study_design_scores_gemma":[0.001121804,0.00009763772,0.2589774,0.0000470263,0.0003722857,0.000003407602,0.0000904278,0.0002791993,0.7378809,0.0001774158,0.000635346,0.0003171396],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877465,0.01129755,0.00003445036,0.00004810298,0.00004472199,0.0002199076,0.0002651828,0.000004572806,0.0003389458],"genre_scores_gemma":[0.9917504,0.000689525,0.006759511,0.0001236315,0.0002791977,0.00002225035,0.0002595957,0.0000299544,0.00008592635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02457292,"threshold_uncertainty_score":0.7273387,"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."}}