{"id":"W6961047676","doi":"10.1371/journal.pone.0266829.t006","title":"Contrasts from logistic regression models&lt;sup&gt;a,b,c&lt;/sup&gt; (Table 5) assessing the statistically significant associations between source type and the occurrence of select antimicrobial resistance genes in phenotypically resistant &lt;i&gt;Escherichia coli&lt;/i&gt; isolates collected from wildlife, swine manure pits, and environmental sources in southern Ontario, 2011−2013 (n = 200, dataset A).","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Wheat and Barley Genetics and Pathology","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Logistic regression; Antimicrobial; Antibiotic resistance; Gene; Genotype; Drug resistance; Type (biology)","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":[0.003280988,0.001924875,0.001442527,0.001727888,0.00120769,0.001285912,0.002689176,0.00101075,0.06903589],"category_scores_gemma":[0.01630457,0.0006952318,0.003016635,0.003244392,0.0005837579,0.0008821002,0.001422701,0.001806537,0.0175578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002866886,"about_ca_system_score_gemma":0.005950666,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.325132,"about_ca_topic_score_gemma":0.5109324,"domain_scores_codex":[0.9979419,0.000515992,0.0001649881,0.0008141748,0.0002909342,0.0002719211],"domain_scores_gemma":[0.9905125,0.005881577,0.0006584598,0.001027521,0.001538042,0.0003819009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007858049,0.00009757714,0.08570666,0.002689159,0.001387459,0.0001967777,0.0002305084,0.002303396,0.0009414005,0.0007107966,0.8943515,0.01059899],"study_design_scores_gemma":[0.002038552,0.0003065039,0.3677006,0.001169902,0.002593482,0.0003561905,0.001099581,0.004885528,0.0009438063,0.002075427,0.61667,0.0001602884],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005132231,0.0002167387,0.0005075883,0.0001914961,0.00006845673,0.00005190802,0.9923193,0.000356039,0.001156192],"genre_scores_gemma":[0.019528,0.0001502871,0.001840859,0.000129227,0.00002580656,0.0003914825,0.9725466,0.0003139837,0.005073793],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.674868,"threshold_uncertainty_score":0.646479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0228905382093777,"score_gpt":0.2208336622240098,"score_spread":0.1979431240146321,"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."}}