{"id":"W2899989631","doi":"10.1038/nbt.4284","title":"Metagenomics meets read clouds","year":2018,"lang":"en","type":"article","venue":"Nature Biotechnology","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Metagenomics; Computational biology; Biology; Data science; Computer science; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":["research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002085806,0.0001348726,0.0001799099,0.00005509763,0.0002495454,0.000004787765,0.0007093658,0.001769277,0.0104726],"category_scores_gemma":[0.0001043405,0.0001224978,0.00004671573,0.0002883269,0.001249052,0.00004455142,0.0006535877,0.001124367,0.002195549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007586455,"about_ca_system_score_gemma":0.000009351907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006087379,"about_ca_topic_score_gemma":0.001001254,"domain_scores_codex":[0.9990922,0.00009550596,0.0001337466,0.0003048256,0.00005035576,0.0003233656],"domain_scores_gemma":[0.999206,0.00005057008,0.0000670636,0.0006277,0.000009759597,0.0000389046],"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":[0.00007900206,0.00009805405,0.0009433506,0.000002531238,0.00004249782,0.00001223739,0.000158324,0.000006703607,0.888161,0.02636051,0.06235254,0.02178327],"study_design_scores_gemma":[0.0001944267,0.0002972549,0.0033522,0.000001749321,0.00001476217,0.00008733614,0.00002059001,0.00003972146,0.09705564,0.007410487,0.8913422,0.0001836085],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9659812,0.0002258026,0.000151917,0.006385028,0.0007840945,0.000144398,0.000009063414,0.0002330589,0.02608538],"genre_scores_gemma":[0.9930265,0.0000821609,0.003425671,0.002657695,0.0001042034,0.000003934021,0.00001494244,0.00001189228,0.0006729763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8289897,"threshold_uncertainty_score":0.9995266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005571068054967026,"score_gpt":0.2362402491035476,"score_spread":0.2306691810485806,"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."}}