{"id":"W3107707615","doi":"10.3168/jds.2020-19325","title":"Genetic diversity and molecular epidemiology of outbreaks of Klebsiella pneumoniae mastitis on two large Chinese dairy farms","year":2020,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Milk Quality and Mastitis in Dairy Cows","field":"Agricultural and Biological Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Guelph","funders":"National Key Research and Development Program of China; Beijing Municipal Natural Science Foundation; National Natural Science Foundation of China","keywords":"Milking; Veterinary medicine; Mastitis; Biology; Genetic diversity; Diversity index; Feces; Klebsiella pneumoniae; Outbreak; Dairy cattle; Barn; Animal science; Microbiology; Medicine; Ecology; Virology; Geography; Species richness; Population; Environmental health; Escherichia coli","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":[0.0008202936,0.0004410305,0.0004514145,0.001960507,0.0009887867,0.0006169651,0.0006085551,0.0006469628,0.0004628905],"category_scores_gemma":[0.001251522,0.0005181392,0.0004540058,0.001412077,0.0009826224,0.0003071855,0.0008385825,0.0002983951,0.00007227818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001981144,"about_ca_system_score_gemma":0.00113776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03148086,"about_ca_topic_score_gemma":0.0351435,"domain_scores_codex":[0.9989434,0.0001920496,0.0001027594,0.0003515732,0.0001859877,0.0002242753],"domain_scores_gemma":[0.998695,0.0002506331,0.000407592,0.00008361885,0.000189753,0.0003734182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001906139,0.00006964515,0.9825833,0.00002045181,0.00007564511,0.0007615164,0.001912308,0.0001233871,0.01232411,0.00002794802,0.00004482295,0.001866094],"study_design_scores_gemma":[0.000005638867,0.00006740257,0.9988815,0.000002544367,0.00001280491,0.0001415414,0.0005927344,0.0001172334,0.0001249113,0.000005710611,0.00004359613,0.00000434138],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9998977,0.00001361108,0.00002083867,0.000006927392,4.716164e-7,0.000003833828,0.0000196267,6.597464e-7,0.00003640416],"genre_scores_gemma":[0.9996985,0.00002619025,0.00006555919,0.00001397407,0.000002218947,0.00001048882,0.0001114819,6.613598e-7,0.00007084712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03148086,"threshold_uncertainty_score":0.06259525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03628422479323903,"score_gpt":0.2741305446779386,"score_spread":0.2378463198846995,"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."}}