{"id":"W2953329757","doi":"10.1101/046086","title":"Nanocall: An Open Source Basecaller for Oxford Nanopore Sequencing Data","year":2016,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"Canadian Institutes of Health Research; Prostate Cancer Canada; Government of Ontario; Oxford Nanopore Technologies; Ontario Institute for Cancer Research; Movember Foundation","keywords":"Nanopore sequencing; Computer science; DNA sequencing; MIT License; Nanopore; Open source; Computational biology; Cloud computing; DNA sequencer; License; DNA; Biology; Nanotechnology; Genetics; Operating system; Software","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008027737,0.0006186602,0.0005655786,0.00007532453,0.0002933813,0.0002897416,0.003101715,0.0006332607,0.0000231571],"category_scores_gemma":[0.0002403785,0.0005722473,0.0001211227,0.00008589336,0.0001482794,0.000009044594,0.005872237,0.0002245859,0.000008433421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008763987,"about_ca_system_score_gemma":0.000669017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000454453,"about_ca_topic_score_gemma":0.00002562067,"domain_scores_codex":[0.9965718,0.0001143188,0.0005203067,0.001928497,0.0001983109,0.0006667272],"domain_scores_gemma":[0.9950821,0.00003365933,0.0003892084,0.003767098,0.0004389283,0.0002889532],"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":[0.00007168946,0.00007473747,0.001194672,0.0001414344,0.0003240409,0.000004763515,0.000006804558,0.00002951612,0.9950277,0.0001679113,0.00292944,0.00002728124],"study_design_scores_gemma":[0.001459857,0.0004228099,0.003445194,0.0002480672,0.000206926,6.670815e-8,0.00001162269,0.00081541,0.4870759,0.0000238109,0.5046036,0.001686818],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9689925,0.003798769,0.0189606,0.0004450043,0.00139729,0.002286472,0.003980591,0.00006485065,0.0000739326],"genre_scores_gemma":[0.980689,0.001021735,0.0156731,0.0005948117,0.001279433,0.0003978091,0.00001735595,0.0002443053,0.00008245152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5079519,"threshold_uncertainty_score":0.9996729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04300101391161978,"score_gpt":0.26662559309903,"score_spread":0.2236245791874102,"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."}}