Clinical mastitis: Incidence, etiology and treatment in organic and conventional dairy herds
Bibliographic record
Abstract
ABSTRACT \n \nOrganic food production is a growing sector worldwide and anticipates to the consumer growing demand for food produced on a sustainable way, without the use of antibiotics, chemicals and pesticides, and better animal health and welfare. This report compares health and welfare between organic and conventional dairy farms by a literature review and a field study done in Alberta, Canada, with emphasis on clinical mastitis incidence, etiology and antibiotic usage. Animal welfare in organic farming is on average better than in conventional dairy farming, but health is not necessarily better. Organic regulations and management standards cannot guarantee better health because they do not influence management style. In most studies on health and welfare in organic dairy farming, mastitis is used as a parameter to measure animal health. In these studies, incidence of clinical mastitis varied from a lower incidence to a same or even higher incidence rate on organic farms compared to conventional. In several studies, distribution of mastitis pathogens cultured from cases of clinical mastitis showed a slight tendency towards more contagious pathogens in organic farms compared to conventional farms. Antibiotic resistance is expected to be lower on organic farms, but no general higher susceptibility of mastitis pathogens to antimicrobial drugs was proven in any of the studies, except in the USA, where difference in antibiotic usage between organic and conventional dairy farms is very high. \nIn the field study, which was performed within the Alberta Organic Dairy Research Project, all clinical mastitis cases in organic farms were sampled and cultured over a period of 2 months. Results were then compared with data of conventional farms obtained from a national cohort study about udder health, performed by the Canadian Bovine Mastitis Research Network (CBMRN). Unfortunately, no associations with production method (organic vs. conventional) could be made for incidence and etiology of clinical mastitis and antibiotic resistance at this time, because of the low number of samples collected so far. \n \nAbbreviation key: CNS = coagulase-negative staphylococci, CON = conventional, IRCM = incidence rate of clinical mastitis, ORG = organic
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".