{"id":"W4393789730","doi":"10.5281/zenodo.7683060","title":"MinION plasmid deep long read sequencing for sequence verification","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Minion; Plasmid; Sequence (biology); Computer science; Nanopore sequencing; Computational biology; Biology; DNA sequencing; Genetics; DNA","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":[0.00449839,0.003227785,0.002791791,0.003883878,0.001748523,0.003230128,0.004225881,0.002960862,0.05960961],"category_scores_gemma":[0.009108175,0.001167548,0.001594281,0.00558376,0.0007406492,0.001800302,0.002439059,0.00309713,0.111245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001481899,"about_ca_system_score_gemma":0.003112139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006760796,"about_ca_topic_score_gemma":0.01580791,"domain_scores_codex":[0.9963831,0.0007009192,0.0004102876,0.001564916,0.0006202518,0.0003205453],"domain_scores_gemma":[0.9968961,0.0009707999,0.0002972199,0.001077722,0.0005358468,0.0002222631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005202093,0.00007447079,0.002811698,0.003544662,0.0002399819,0.0001563932,0.0001445183,0.001437422,0.004376129,0.002933077,0.9738349,0.009926589],"study_design_scores_gemma":[0.000512617,0.00006815354,0.004372146,0.0004883038,0.000149533,0.0001620528,0.00009063212,0.0009283977,0.004316176,0.005876502,0.9829566,0.00007899618],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003419177,0.0001141632,0.0008297628,0.00003843787,0.00002277403,0.00002376856,0.9967459,0.001093923,0.0007892571],"genre_scores_gemma":[0.0005192793,0.00005618183,0.001830355,0.00005628195,0.000004404293,0.0001535931,0.9964384,0.0004041828,0.0005374097],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05960961,"threshold_uncertainty_score":0.199414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05311033717732835,"score_gpt":0.3001967635508366,"score_spread":0.2470864263735082,"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."}}