Milk losses and quality payment associated with somatic cell counts under different management systems in an arid climate
Bibliographic record
Abstract
Sadeghi-Sefidmazgi, A. and Amer, P. R. 2015. Milk losses and quality payment associated with somatic cell counts under different management systems in an arid climate. Can. J. Anim. Sci. 95: 351–360. The objectives of this research were (1) to estimate the economic benefits or new marketing opportunities due to a reduction in milk somatic cell count (SCC) for dairy producers through improved management practices and (2) to quantify the production loss associated with SCC under different management systems. A total of 38 530 average lactation SCC records for 10 216 Holstein cows gathered on 25 dairy farms from January 2009 to October 2012 in Isfahan (Iran) were analyzed under 13 types of herd management practices including 40 levels of health, milking and housing conditions. The results show that there are many well-established management practices associated with higher-quality payment for SCC that have not yet been applied in Isfahan dairy farms. The lowest and highest economic premium opportunity (US$) from SCC were estimated to be for production systems applying washable towels for teat cleaning (5.69) and production systems with no teat disinfection (31.07) per cow per lactation. Results indicate that any increase of one unit in average lactation somatic cell score is expected to cause a significant reduction in average lactation 305-d milk yield from 36.0 to 173.4 kg, depending on the level of management practices employed. In general, farmers with higher milk yield and well-managed practices for mastitis control would lose more milk when an increase occurs in SCC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".