{"id":"W2010002522","doi":"10.1186/gb-2012-13-12-r122","title":"Ray Meta: scalable de novo metagenome assembly and profiling","year":2012,"lang":"en","type":"article","venue":"Genome biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":620,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Université Laval","funders":"Canadian Institutes of Health Research; Mitacs; Québec Consortium for Drug Discovery; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke; Fonds Québécois de la Recherche sur la Nature et les Technologies; McGill University; Université Laval","keywords":"Biology; Metagenomics; Computational biology; Human genetics; Profiling (computer programming); Genome Biology; Genetics; Evolutionary biology; Genomics; Bioinformatics; Genome; Computer science; Gene","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":[0.002167869,0.002767485,0.001683095,0.001735994,0.001066123,0.002325909,0.003218425,0.001105638,0.008175427],"category_scores_gemma":[0.003502507,0.002011889,0.002132538,0.001641286,0.0004538244,0.001838562,0.003129604,0.002653243,0.008644693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008696102,"about_ca_system_score_gemma":0.001744056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003062967,"about_ca_topic_score_gemma":0.004288835,"domain_scores_codex":[0.998777,0.0002048483,0.00007566952,0.0003824586,0.0004295208,0.0001304677],"domain_scores_gemma":[0.9987425,0.0003092094,0.0001370153,0.0004517272,0.0002023023,0.0001572534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005185511,0.0005375043,0.01060932,0.003129781,0.002018202,0.0007341385,0.001183626,0.03659831,0.4593018,0.01122485,0.2248528,0.2446242],"study_design_scores_gemma":[0.001162167,0.0005739296,0.01033431,0.0002704125,0.0003897012,0.0009447117,0.0002697174,0.416732,0.3264658,0.01576744,0.2265184,0.0005714394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03794654,0.001508047,0.5544373,0.0003949031,0.0003383066,0.0008515741,0.04536839,0.3501015,0.009053481],"genre_scores_gemma":[0.07374616,0.0007938424,0.8078765,0.0003451284,0.00009331825,0.001410746,0.08151532,0.02905827,0.00516062],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008175427,"threshold_uncertainty_score":0.02734953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02834397645890653,"score_gpt":0.2629154214458959,"score_spread":0.2345714449869894,"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."}}