{"id":"W2951092782","doi":"10.3389/fmicb.2017.01461","title":"Fast and Simple Analysis of MiSeq Amplicon Sequencing Data with MetaAmp","year":2017,"lang":"en","type":"article","venue":"Frontiers in Microbiology","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Genome Alberta; Canada First Research Excellence Fund; Genome Prairie; Alberta Innovates; Government of Canada; Natural Sciences and Engineering Research Council of Canada; Government of Alberta; Research Manitoba; Genome Canada","keywords":"Amplicon sequencing; Amplicon; Metagenomics; Shotgun sequencing; Computer science; DNA sequencing; Computational biology; Biology; Massive parallel sequencing; Data mining; Gene; 16S ribosomal RNA; Genetics; Polymerase chain reaction","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":[0.005778175,0.00428624,0.003067773,0.004110078,0.001717639,0.002987674,0.002996519,0.001355978,0.009215725],"category_scores_gemma":[0.01265819,0.002445922,0.003095458,0.003548651,0.0008843906,0.002027553,0.002843388,0.004676946,0.01569281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028065,"about_ca_system_score_gemma":0.002021791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001065298,"about_ca_topic_score_gemma":0.002463407,"domain_scores_codex":[0.9934082,0.001373572,0.0009323864,0.0018655,0.002013548,0.0004068407],"domain_scores_gemma":[0.9960808,0.001291135,0.00049443,0.0007830781,0.001169971,0.0001806006],"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":[0.002171041,0.000460466,0.008041179,0.005708487,0.00138348,0.0008036177,0.001797436,0.005780309,0.6688317,0.008000456,0.04253649,0.2544852],"study_design_scores_gemma":[0.0002473479,0.0009589343,0.01576438,0.0008133249,0.000492931,0.001369009,0.0004122042,0.0760791,0.6332614,0.01294614,0.2568229,0.0008322368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0197371,0.001641963,0.9001246,0.0003414316,0.0005602857,0.001614421,0.03080563,0.03998711,0.005187321],"genre_scores_gemma":[0.01588216,0.0004798076,0.9557374,0.000299538,0.00007121677,0.002862856,0.01690859,0.005370907,0.002387677],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009215725,"threshold_uncertainty_score":0.03082961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02392921009340367,"score_gpt":0.2563964546598616,"score_spread":0.2324672445664579,"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."}}