{"id":"W2945250978","doi":"10.1101/645903","title":"Enabling high-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Poul Due Jensens Fond; Genome British Columbia; Villum Fonden","keywords":"Nanopore sequencing; Amplicon; Identifier; Amplicon sequencing; Computational biology; DNA sequencing; Nanopore; Computer science; Biology; Genetics; Gene; Polymerase chain reaction; Nanotechnology; Computer network; Materials science","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":[0.001751165,0.0006094847,0.000776468,0.0005893779,0.0004400109,0.001173505,0.0007113114,0.001019912,0.001651371],"category_scores_gemma":[0.002077234,0.0005669173,0.0003759205,0.0004566507,0.0005615492,0.001054841,0.001104488,0.001484062,0.002199892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004617106,"about_ca_system_score_gemma":0.0005801959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006037764,"about_ca_topic_score_gemma":0.001308583,"domain_scores_codex":[0.9987374,0.0002341079,0.0001011883,0.0003803281,0.0004273102,0.0001196383],"domain_scores_gemma":[0.9989651,0.0003543591,0.0001548002,0.0002399214,0.0001871274,0.0000986553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005794243,0.00001943918,0.0008699599,0.0001360903,0.00002378211,0.00008518107,0.00008355957,0.0006042031,0.9805115,0.001689891,0.0006850104,0.01523348],"study_design_scores_gemma":[0.000004432298,0.00003583828,0.001078841,0.00001728917,0.00001678142,0.0003114835,0.00003385947,0.00575429,0.9809942,0.001034167,0.01069675,0.00002195106],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2444929,0.001958013,0.7389684,0.0006314064,0.0002523377,0.0001895566,0.002806957,0.005665661,0.00503464],"genre_scores_gemma":[0.2625728,0.001233769,0.7266533,0.0003494414,0.00006526106,0.0001629253,0.004116871,0.0008169074,0.004028694],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001751165,"threshold_uncertainty_score":0.009261191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01998739810877913,"score_gpt":0.2385796714985765,"score_spread":0.2185922733897973,"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."}}