{"id":"W2620002992","doi":"10.12688/f1000research.11354.1","title":"MinION Analysis and Reference Consortium: Phase 2 data release and analysis of R9.0 chemistry","year":2017,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Nanopore and Nanochannel Transport Studies","field":"Engineering","cited_by":147,"is_retracted":false,"has_abstract":true,"ca_institutions":"Michael Smith Health Research BC; University of British Columbia","funders":"National Science Foundation of Sri Lanka; Biotechnology and Biological Sciences Research Council; Rosetrees Trust; National Science Foundation; Wellcome; Canadian Institutes of Health Research; National Institute for Health and Care Research; Genome British Columbia; Michael Smith Health Research BC; Oxford Nanopore Technologies; National Human Genome Research Institute; Wellcome Trust; Fondation Brain Canada; Medical Research Council","keywords":"Minion; Nanopore sequencing; Computational biology; Biology; Chemistry; Genome; Computer science; Algorithm; Genetics; Gene","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":[0.02795196,0.003460816,0.002235693,0.00347929,0.001947229,0.003780963,0.005953693,0.00150579,0.0421573],"category_scores_gemma":[0.04765778,0.002254355,0.002288383,0.003191415,0.001470734,0.002531619,0.003748973,0.004005761,0.06208917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001615597,"about_ca_system_score_gemma":0.00775331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004085384,"about_ca_topic_score_gemma":0.004329376,"domain_scores_codex":[0.9879703,0.003333806,0.00148841,0.002316963,0.00397847,0.0009119971],"domain_scores_gemma":[0.9761624,0.006519023,0.001638903,0.007660612,0.007025236,0.0009937483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005880826,0.0005763987,0.008167826,0.003353054,0.0006508081,0.0003721267,0.0009298971,0.005485452,0.06892921,0.01559495,0.8146846,0.07537492],"study_design_scores_gemma":[0.001633709,0.001062875,0.01672306,0.0006353965,0.0003253097,0.0004228413,0.0002115997,0.02309324,0.1719535,0.01186589,0.7715738,0.0004986695],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.02104863,0.000479776,0.3157436,0.001133392,0.0007301642,0.004065003,0.369297,0.2711575,0.01634486],"genre_scores_gemma":[0.02244277,0.0002205934,0.3006802,0.0009678095,0.0001121543,0.007657906,0.5492058,0.1097984,0.008914344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0421573,"threshold_uncertainty_score":0.1478259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1087761537316273,"score_gpt":0.3762056178605001,"score_spread":0.2674294641288727,"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."}}