Effects of dry period management and parity on rumen fermentation, blood metabolites, and liver triacylglyceride in dairy cows
Bibliographic record
Abstract
Khazanehei, H., Li, S., Khafipour, E. and Plaizier, J. C. 2015. Effects of dry period management and parity on rumen fermentation, blood metabolites, and liver triacylglyceride in dairy cows. Can. J. Anim. Sci. 95: 445–453. The effects of dry period management on rumen fermentation, blood metabolites and liver triacylglyceride (TAG) were determined in 11 second-parity (PAR 2) and 15 third-parity and older (PAR 3+) cows. Cows were paired and randomly assigned to two treatments: (1) a short 40-d dry period (SHORT) with only a close-up diet [1.43 Mcal kg–1 dry matter (DM)], or (2) a conventional 60-d dry period (CONV) with 39 d far-off diet (1.28 Mcal kg–1 DM) and 21 d close-up diet (1.43 Mcal kg–1 DM) in a randomized block design. Concentrations of total volatile fatty acids, ammonia, and lactate in rumen fluid, and also concentrations of lactate, beta hydroxybutyric acid (BHBA), urea and insulin in plasma were not affected by treatment. Across the 3 wk after calving, cows on the SHORT treatment had higher blood non-esterified fatty acid (NEFA) and glucose, and at 1 wk after calving, these cows had a higher liver TAG than cows on the CONV treatment. Also, during this time, PAR 3+ cows had higher serum NEFA compared with PAR 2 cows. Results suggest that the SHORT treatment resulted in more lipolysis, TAG accumulation in the liver and glucose sparing in early lactation compared with the CONV treatment, and that this effect was greater in PAR 3+ cows than in PAR 2 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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".