Evaluation of relationship between ruminal pH and the proportion of de novo fatty acids in milk
Bibliographic record
Abstract
The objective of this study was to evaluate the relationship between ruminal pH and milk de novo fatty acid (DNFA) concentrations determined by Fourier-transform infrared spectroscopy. Data were collected from 18 multiparous Holstein cows fitted with a rumen cannula and fed 1 of the experimental diets differing in starch content (22.1 vs. 28.3%) with or without supplementation of a Saccharomyces cerevisiae fermentation product in a previous study. Milk was sampled on d 7 and 21 after calving, and concentrations of milk fat, DNFA (C6 to C14), mixed-origin fatty acids (FA; C16:0 and C16:1), and preformed FA (≥C18) were estimated using Fourier-transform infrared spectrometry. Ruminal pH was recorded in the ventral sac every 30 s continuously for 72 h on d 7 to 9 and 21 to 23 after calving. Daily maximum, nadir, and mean ruminal pH as well as duration and area below pH 5.8 were determined for each period. Milk DNFA (g/100 g of FA) was positively related to nadir (r = 0.428) and mean (r = 0.471) ruminal pH and negatively related to duration (r = −0.511) and area (r = −0.520) below pH 5.8. Milk fat content did not have a relationship with ruminal pH variables in this study. The regression lines for d 7 and 21 were similar, likely because plasma free FA concentrations were not different between d 7 and 21 (513 vs. 534 µEq/L) for the current data set. The coefficients of determination between DNFA and ruminal pH were greater for DNFA in total milk FA (g/100 g of FA) than in milk (g/100 g of milk), suggesting that DNFA in milk fat (g/100 g of FA) is an appropriate measurement variable that relates to ruminal pH even for cows in early lactation.
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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 | 0.000 |
| 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".