{"id":"W4306749986","doi":"10.1101/2022.10.18.512610","title":"<i>SituSeq</i> : An offline protocol for rapid and remote Nanopore amplicon sequence analysis","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada; Government of Nova Scotia; University of Calgary","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Genome Alberta; Genome Atlantic; Alberta Innovates; Mitacs; Genome Canada","keywords":"Amplicon; Nanopore sequencing; Metagenomics; Minion; Computational biology; 16S ribosomal RNA; Biology; Workflow; Sequence analysis; DNA sequencing; Genetics; Computer science; Gene; Polymerase chain reaction; Database","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":[0.004272655,0.003769095,0.002285264,0.003331914,0.001977588,0.001491306,0.003158649,0.001761781,0.05970604],"category_scores_gemma":[0.007081553,0.00257342,0.001931718,0.002330557,0.001522777,0.001702592,0.002265409,0.004514466,0.04979115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006263867,"about_ca_system_score_gemma":0.001916197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006174113,"about_ca_topic_score_gemma":0.001721375,"domain_scores_codex":[0.9950884,0.00125313,0.0009812551,0.001230685,0.0008914551,0.0005550458],"domain_scores_gemma":[0.995759,0.001151836,0.0004309276,0.001276435,0.001106824,0.0002750464],"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":[0.001350748,0.0003150345,0.00146644,0.002559433,0.0002076309,0.001413678,0.0006057413,0.001421552,0.7389329,0.007741202,0.2004665,0.0435192],"study_design_scores_gemma":[0.0004529179,0.0005533618,0.004119655,0.0004873392,0.00009919482,0.0009883642,0.0001483954,0.01166122,0.5439976,0.005025357,0.4320757,0.0003908001],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01656923,0.0009104193,0.8379478,0.0009356895,0.002180037,0.01008346,0.05111203,0.05849293,0.02176853],"genre_scores_gemma":[0.04735026,0.001229925,0.7621211,0.003139676,0.0007560217,0.05378474,0.08836377,0.01749808,0.02575652],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05970604,"threshold_uncertainty_score":0.1997366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03167551335747261,"score_gpt":0.2776738448077925,"score_spread":0.2459983314503199,"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."}}