Effects of dry period management on milk production, dry matter intake, and energy balance of dairy cows
Bibliographic record
Abstract
Khazanehei, H., Li, S., Khafipour, E. and Plaizier, J. C. 2015. Effects of dry period management on milk production, dry matter intake, and energy balance of dairy cows. Can. J. Anim. Sci. 95: 433–444. Effects of dry-period management on milk production, feed intake, and energy balance were determined in 11 second-parity (PAR 2) and 15 third or later parity (PAR 3+) cows. Cows were paired based on expected calving date, and randomly assigned to treatments including a conventional 60-d dry period with a 39-d far-off and a 21-d close-up diet (CONV), and a short 40-d dry period with only a close-up diet (SHORT). Treatment did not affect dry matter intake (DMI) and energy balance across the 6 wk before calving and the 4 wk after calving. Across the first 16 wk of lactation, cows on the short treatment had lower milk yield compared to cows on the CONU treatment that was mostly due to the lower production in PAR 3+ cows. Also, PAR 3+ cows on the SHORT treatment had lower milk protein yields and higher somatic cell counts than the other cows. In general, PAR 2 cows on the SHORT treatment had similar DMI, fat corrected milk yields, and protein and fat yields and percentages compared to cows in both parity groups on the CONV treatment. This suggests that the SHORT treatment may be suitable for PAR 2 cows, but not for older cows.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".