{"id":"W2610351874","doi":"10.1101/131631","title":"Fast and simple analysis of MiSeq amplicon sequencing data with MetaAmp","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Amplicon sequencing; Amplicon; Shotgun sequencing; Metagenomics; Computer science; DNA sequencing; Computational biology; Data mining; Biology; Gene; 16S ribosomal RNA; Genetics; Polymerase chain reaction","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004483357,0.00315927,0.002075215,0.00307975,0.001234942,0.002578989,0.002384817,0.001011242,0.009772315],"category_scores_gemma":[0.00941766,0.001733853,0.00213562,0.002443491,0.0006520545,0.001469341,0.002147388,0.003278255,0.01600147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007880635,"about_ca_system_score_gemma":0.001500213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007894743,"about_ca_topic_score_gemma":0.001580577,"domain_scores_codex":[0.9951885,0.0009000256,0.0006602968,0.001376899,0.001583318,0.0002909677],"domain_scores_gemma":[0.9970821,0.0008488202,0.0003581262,0.0006804726,0.0008894363,0.0001411251],"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":[0.001975924,0.0003113554,0.006632743,0.002840418,0.0008114782,0.0007565803,0.001091058,0.005214699,0.6801364,0.008313514,0.04553619,0.2463795],"study_design_scores_gemma":[0.000169471,0.0005356105,0.01207789,0.0004394984,0.0002607307,0.0008788717,0.0002217782,0.07758826,0.7038216,0.01051458,0.1930006,0.0004911395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.02094478,0.0008073606,0.8938139,0.0002993648,0.0004471695,0.001013,0.02926022,0.04968743,0.003726688],"genre_scores_gemma":[0.02196399,0.0002557907,0.9494563,0.0002208615,0.00005891654,0.002065382,0.01699142,0.006090937,0.002896382],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.009772315,"threshold_uncertainty_score":0.0326916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03278440210650221,"score_gpt":0.2472535636956381,"score_spread":0.2144691615891359,"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."}}