Mastitis, Ketosis, and Milk Fever in 31 Organic and 93 Conventional Norwegian Dairy Herds
Bibliographic record
Abstract
The aim of this study was to investigate differences in disease incidence between organic and conventional herds. The study was based on data from the Norwegian Dairy Herd Recording, which includes the Norwegian Cattle Health Recording System. All herds certified for organic farming in 1994 with a herd size of more than five cow-years were included. Conventional herds were matched on size and region, and from these, three herds were randomly selected for each organic herd. This resulted in a study group of 31 organic and 93 conventional herds with data from 1994 through 1997. The study unit was the cow within a lactation. Factors influencing disease incidence were studied by means of a generalized linear model approach. Management system had a highly significant effect on disease incidence. Odds ratios for organic compared with conventional herds were as follows: mastitis, 0.38; ketosis, 0.33; and milk fever, 0.60. Other significant factors that emerged in modeling the three diseases were year and lactation category for mastitis; lactation category, maximum milk yield, and season for ketosis; and lactation category and milk yield for milk fever. There was no marked difference in milk somatic cell count (SCC) between organic and conventional herds. However, cows in organic herds had lower SCC in lactation two and greater counts in lactations six and higher.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".