{"id":"W2999739138","doi":"10.1101/2020.01.10.902056","title":"Rapid and accurate SNP genotyping of clonal bacterial pathogens with BioHansel","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Public Health Agency of Canada","funders":"Government of Canada; Public Health Agency; Public Health Agency of Canada","keywords":"Genotyping; Computational biology; SNP genotyping; Whole genome sequencing; Contig; Indel; Genome; Biology; Genetics; Single-nucleotide polymorphism; Computer science; Data mining; Genotype","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":[0.002405923,0.0009628531,0.0008134887,0.00225385,0.0007606768,0.001966444,0.001630254,0.0009884805,0.008948308],"category_scores_gemma":[0.003286616,0.0009191569,0.000942563,0.001541715,0.0006106048,0.001547701,0.001783592,0.0009807495,0.007185738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008214482,"about_ca_system_score_gemma":0.0008760231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001701644,"about_ca_topic_score_gemma":0.002920932,"domain_scores_codex":[0.9976161,0.0003781119,0.0001667593,0.0008730748,0.0008086737,0.0001573035],"domain_scores_gemma":[0.997324,0.0008104668,0.0004619079,0.0006981874,0.0005149505,0.0001905557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002279653,0.0001653205,0.01862588,0.001070928,0.0003891419,0.0007029723,0.0008317384,0.008331154,0.6252506,0.006594255,0.06864063,0.2671176],"study_design_scores_gemma":[0.0002088831,0.0003290489,0.02011118,0.0001425304,0.0001792529,0.001019102,0.000327762,0.07831407,0.7408507,0.003678429,0.1544576,0.0003813946],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.251685,0.002644532,0.5296401,0.001731699,0.0006842482,0.0006220741,0.03742942,0.1537234,0.02183953],"genre_scores_gemma":[0.2339164,0.0006424389,0.7067531,0.0009808247,0.0001467061,0.0006240286,0.03438846,0.005843123,0.01670492],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008948308,"threshold_uncertainty_score":0.02993506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01817635105777439,"score_gpt":0.2106483230818156,"score_spread":0.1924719720240411,"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."}}