{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004897873,0.0002070994,0.0003081156,0.0000460092,0.0001286643,0.00001393792,0.0001510946,0.0001822563,0.00002679987],"category_scores_gemma":[0.00004615941,0.0001719658,0.0001107749,0.00006000656,0.0001219323,0.000001408748,0.0002489603,0.000072411,0.00001759007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001403962,"about_ca_system_score_gemma":0.00004194843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001821652,"about_ca_topic_score_gemma":0.000007140205,"domain_scores_codex":[0.9987444,0.00009680584,0.0001944505,0.000332539,0.00003710484,0.0005946677],"domain_scores_gemma":[0.9994421,0.00002126136,0.00006933064,0.0002679667,0.00004693778,0.0001523854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001906614,0.00002947553,0.03003937,0.000009872005,0.0007335432,5.960384e-7,0.00006629597,0.00001676913,0.9679804,0.0007921212,0.00002029328,0.0002922069],"study_design_scores_gemma":[0.001330606,0.000845088,0.1428924,0.000002563376,0.001343934,0.000200233,0.0002796448,0.00002290443,0.3557375,0.001242163,0.494938,0.001164955],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9418568,0.05500326,0.00136629,0.00009980626,0.0001857588,0.0001769381,0.00004663195,0.00000656934,0.001257967],"genre_scores_gemma":[0.9874632,0.001422082,0.009758186,0.0004018041,0.0004047967,0.00006476275,0.00005226308,0.00002455634,0.0004082915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6122429,"threshold_uncertainty_score":0.7012562,"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."}}