{"id":"W2901502410","doi":"10.2172/1481615","title":"Real-Time Automated Pathogen Identification by Enhanced Ribotyping (RAPIER) LDRD Final Report","year":2018,"lang":"en","type":"report","venue":"","topic":"Biosensors and Analytical Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Laboratory Directed Research and Development; National Nuclear Security Administration; Oxford Nanopore Technologies; Sandia National Laboratories; U.S. Department of Energy","keywords":"Minion; Nanopore sequencing; Computer science; Identification (biology); Automation; Engineering; Biology; DNA sequencing","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":[0.006138866,0.001045468,0.0005046651,0.001314636,0.0008455189,0.001787222,0.001852265,0.001064897,0.01270907],"category_scores_gemma":[0.002521364,0.0004514238,0.0004629713,0.0003855786,0.0004252083,0.000929764,0.001521519,0.001221881,0.009020155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002084972,"about_ca_system_score_gemma":0.004207109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007549031,"about_ca_topic_score_gemma":0.01240263,"domain_scores_codex":[0.9941341,0.0008403924,0.0001540388,0.0006523959,0.003724884,0.0004941649],"domain_scores_gemma":[0.9977819,0.0002408194,0.0001331058,0.0002383743,0.001359989,0.0002457978],"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":[0.001968977,0.001930296,0.010892,0.0006968645,0.0001039491,0.001714153,0.0005754785,0.009774795,0.2225767,0.01064865,0.3874571,0.3516611],"study_design_scores_gemma":[0.0001623747,0.001822099,0.00666685,0.00006619571,0.00003390284,0.000865748,0.0001700978,0.009489625,0.315143,0.0008980496,0.6645836,0.00009855432],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.1817994,0.003101391,0.4428739,0.007595814,0.003009906,0.01461856,0.07763731,0.01947565,0.2498881],"genre_scores_gemma":[0.1748621,0.001866718,0.4320937,0.001198033,0.0004016265,0.003281142,0.06745,0.001604349,0.3172424],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01270907,"threshold_uncertainty_score":0.04251611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01631487972736232,"score_gpt":0.2654652262001974,"score_spread":0.249150346472835,"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."}}