{"id":"W840198283","doi":"10.1016/j.ympev.2015.06.007","title":"Assessing host-specificity of Escherichia coli using a supervised learning logic-regression-based analysis of single nucleotide polymorphisms in intergenic regions","year":2015,"lang":"en","type":"article","venue":"Molecular Phylogenetics and Evolution","topic":"Escherichia coli research studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Provincial Laboratory of Public Health; Agriculture and Agri-Food Canada; Environment and Climate Change Canada; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biology; Intergenic region; Host (biology); Genetics; Single-nucleotide polymorphism; Escherichia coli; Genetic marker; SNP; Computational biology; Gene; Genotype; Genome","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":[0.001100539,0.0003751685,0.0003884981,0.0007967884,0.0002131573,0.0005038922,0.0004656241,0.0003937715,0.0003681672],"category_scores_gemma":[0.002004272,0.0001607793,0.0005417239,0.0004121692,0.000202189,0.0002865966,0.0002509058,0.0004682629,0.0001738789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003131951,"about_ca_system_score_gemma":0.0003195467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001541304,"about_ca_topic_score_gemma":0.001943744,"domain_scores_codex":[0.9994882,0.000170603,0.00003709345,0.0001846073,0.00008024665,0.00003911518],"domain_scores_gemma":[0.9986059,0.0008726959,0.0002237557,0.0000610198,0.000186523,0.00005009185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001004049,0.0006718633,0.1415422,0.0002058867,0.0003361962,0.0002315937,0.0001625593,0.2059655,0.4355866,0.00132728,0.0003547034,0.2126116],"study_design_scores_gemma":[0.00000901058,0.0001289174,0.02198719,0.00000479136,0.00004392513,0.00008240095,0.00003028022,0.9502347,0.02682714,0.0005114765,0.0001259255,0.0000142243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8519393,0.0001070404,0.1468924,0.00003979694,0.000005394325,0.0000230282,0.0002098591,0.0003571193,0.0004260614],"genre_scores_gemma":[0.952449,0.00003071364,0.04692094,0.00001383198,0.000003850405,0.00001431754,0.0003281095,0.00001815003,0.0002210233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001541304,"threshold_uncertainty_score":0.005820274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04349690621230002,"score_gpt":0.29992358827274,"score_spread":0.25642668206044,"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."}}