{"id":"W6960859050","doi":"10.1371/journal.pone.0260234.t007","title":"Univariable logistic regression models<sup>a</sup><sup>,</sup><sup>b</sup><sup>,</sup><sup>c</sup> assessing the association between source type, farm location, and year of sampling, and the occurrence of select antimicrobial resistance genes and plasmid incompatibility (Inc) types in phenotypically resistant <i>Escherichia coli</i> isolates obtained from raccoons, swine manure pits, and soil samples on swine farms in southern Ontario, Canada 2011–2013 (n = 96).","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Logistic regression; Plasmid; Antimicrobial; Gene; Antibiotic resistance; Manure; Genotype; Selection (genetic algorithm)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005572905,0.002069185,0.001278502,0.001100326,0.0008718843,0.001532528,0.003400422,0.001221931,0.03133932],"category_scores_gemma":[0.01548533,0.0008539228,0.002924679,0.002069894,0.0005939411,0.001310348,0.001630927,0.003150478,0.004854443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003988378,"about_ca_system_score_gemma":0.009391854,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4773,"about_ca_topic_score_gemma":0.5082976,"domain_scores_codex":[0.9964861,0.001292622,0.0002665382,0.0008909008,0.0004089776,0.0006548068],"domain_scores_gemma":[0.9915407,0.004540483,0.0008810013,0.0007414099,0.001848629,0.000447756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003650954,0.0006713273,0.7475919,0.001598318,0.004281424,0.0008957161,0.001497595,0.01628137,0.002455073,0.006348554,0.1503075,0.06442037],"study_design_scores_gemma":[0.0005737772,0.0022892,0.6170489,0.001218075,0.005641547,0.0008766631,0.004740969,0.1702551,0.002599606,0.006889925,0.1875189,0.0003473693],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.6066352,0.004810842,0.07072856,0.005767222,0.002573573,0.002147462,0.2831002,0.002923653,0.02131327],"genre_scores_gemma":[0.7915744,0.001495683,0.0364072,0.0007213278,0.0002619006,0.002406268,0.07798544,0.0009437048,0.08820423],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.5227001,"threshold_uncertainty_score":0.9490435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01933334549757836,"score_gpt":0.2284501770455371,"score_spread":0.2091168315479587,"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."}}