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Record W2606049286 · doi:10.3168/jds.2016-12334

Incidence of clinical mastitis and distribution of pathogens on large Chinese dairy farms

2017· article· en· W2606049286 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Dairy Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsStreptococcus dysgalactiaeStreptococcus agalactiaeMastitisStreptococcus uberisVeterinary medicineHerdIncidence (geometry)BiologyCoagulaseStaphylococcus aureusMicrobiologyStaphylococcusStreptococcusMedicineBacteria

Abstract

fetched live from OpenAlex

Knowledge of the incidence of clinical mastitis (CM) and the distribution of pathogens involved is essential for development of prevention and control programs as well as treatment protocols. No country-wide study on the incidence of CM and the distribution of pathogens involved has been conducted in China. Core objectives of this study were, therefore, to determine the cumulative incidence of CM and the distribution of pathogens causing CM on large Chinese (>500 cows) dairy farms. In addition, associations between the distribution of CM pathogens and bedding materials and seasonal factors were also investigated. Bacterial culture was done on a total of 3,288 CM quarter milk samples from 161 dairy herds (located in 21 provinces) between March 2014 and September 2016. Additional data, including geographical region of herds, herd size, bedding types, and number of CM cases during the last month, were also recorded. Mean cumulative incidence of CM was 3.3 cases per 100 cows per month (range = 1.7 to 8.1). The most frequently isolated pathogens were Escherichia coli (14.4%), Klebsiella spp. (13.0%), coagulase-negative staphylococci (11.3%), Streptococcus dysgalactiae (10.5%), and Staphylococcus aureus (10.2%). Streptococcus agalactiae was isolated from 2.8% of CM samples, whereas Streptococcus uberis were isolated from 2.1% of samples, and 15.8% of 3,288 samples were culture-negative. Coagulase-negative staphylococci, E. coli, and other Enterobacter spp. were more frequently isolated in the northwest than the northeast or south of China. Streptococcus dysgalactiae, other streptococci, and Strep. agalactiae were more frequently isolated in winter (October-March), whereas E. coli and Klebsiella spp. were mostly isolated in summer (April-September). Streptococcus dysgalactiae was more often isolated from CM cases of herds using sand bedding, whereas Klebsiella spp. and other streptococci were more common in herds using organic bedding. The incidence of CM and distribution of pathogens differed among herds and better mastitis management is needed. Furthermore, geography, bedding materials, and season should be included when designing mastitis control and prevention schemes for Chinese dairies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.342
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it