{"id":"W2564341476","doi":"10.1128/msystems.00133-16","title":"SSUnique: Detecting Sequence Novelty in Microbiome Surveys","year":2016,"lang":"en","type":"article","venue":"mSystems","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Novelty; Microbiome; Sequence (biology); Computational biology; Data science; Human Microbiome Project; Computer science; Evolutionary biology; Biology; Geography; Human microbiome; Bioinformatics; Psychology; Genetics","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":[0.00875104,0.002073807,0.001793722,0.006671102,0.001569092,0.00377295,0.001889817,0.001944585,0.004166135],"category_scores_gemma":[0.01722884,0.001311861,0.002230839,0.003594984,0.001303126,0.002985696,0.004337291,0.00182165,0.00408753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006721206,"about_ca_system_score_gemma":0.001695006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000881588,"about_ca_topic_score_gemma":0.002064514,"domain_scores_codex":[0.9945899,0.001295805,0.0005134889,0.00175905,0.001478743,0.0003629903],"domain_scores_gemma":[0.9925852,0.003253871,0.001754135,0.001198601,0.000692229,0.0005159395],"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":[0.003050691,0.0005469588,0.1422542,0.002715481,0.002237456,0.001240755,0.001953889,0.005080855,0.4306493,0.008773776,0.04028542,0.3612113],"study_design_scores_gemma":[0.0004792186,0.001133521,0.1456454,0.0003911317,0.0006353315,0.004335311,0.001148582,0.241281,0.4604816,0.02674797,0.1168473,0.0008735384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1687364,0.001782102,0.7137271,0.0005680327,0.000355635,0.0009054873,0.0276648,0.08239502,0.003865397],"genre_scores_gemma":[0.1433882,0.0005272343,0.8236361,0.0005248403,0.000155743,0.001411076,0.02386515,0.004177429,0.002314358],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00875104,"threshold_uncertainty_score":0.04628044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02618907585529315,"score_gpt":0.2839177500687118,"score_spread":0.2577286742134187,"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."}}