Incidence rate of pathogen-specific clinical mastitis on conventional and organic Canadian dairy farms
Bibliographic record
Abstract
Mastitis is a common and costly production disease on dairy farms. In Canada, the incidence rate of clinical mastitis (IRCM) has been determined for conventionally managed dairy farms; however, no studies to date have assessed rates in organically managed systems. The objectives of this observational study were (1) to determine the producer-reported IRCM and predominant pathogen types on conventional and organic dairy farms in Southern Ontario, Canada, and (2) to evaluate the association of both mean overall IRCM and pathogen-specific IRCM with management system, housing type, and pasture access. Data from 59 dairy farms in Southern Ontario, Canada, distributed across conventional (n=41) and organic management (n=18) systems, were collected from April 2011 to May 2012. In addition to management system, farms were categorized by housing method (loose or tie-stall) and pasture access for lactating cows. Participating producers identified and collected samples from 936 cases of clinical mastitis. The most frequently isolated mastitis pathogens were coagulase-negative staphylococci, Bacillus spp., Streptococcus spp., Staphylococcus aureus, and Escherichia coli. The IRCM was higher on conventional farms than organic (23.7 vs. 13.2 cases per 100 cow-years) and was not associated with housing type (loose or tie-stall), pasture access, or herd-average milk yield. Bulk tank somatic cell count tended to be lower on conventional farms than organic (222,000 vs. 272,000 cells/mL). Pathogen-specific IRCM attributed to Staph. aureus, Bacillus spp., and E. coli was greater on conventional than organic farms, but was not associated with housing or any other factors. In conclusion, organic management was associated with reduced overall and pathogen-specific IRCM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".