Stability and Effects of Protected Palmitic Acid on In Vitro Rumen Degradability and Fermentation in Lactating Goats
Bibliographic record
Abstract
Few studies have evaluated the impact of a lipid-rich diet with palmitic acid (PA) in ruminal fermentation. The objective was to evaluate the in vitro bath culture of the protected PA on the ruminal fermentative variables. Four diets were used: (a) without protected PA (nPA) and (b) inclusion of protected PA at three levels: PA3 = 3%, PA6 = 6%, and PA9 = 9% dry matter (DM). The maximum gas production occurred without including protected PA and the low gas production given with the inclusion of protected PA9 (p ≤ 0.05). Meanwhile, the inclusion with 3 and 6% of protected PA showed a higher level of gas production kinetics than the other treatments (p ≤ 0.05). DM and organic matter degradation after 72 h of incubation linearly decreased (p ≤ 0.05) with a high level of protected PA. The level of C16:0 in the protected AP increased with the higher level of supplemented AP (p ≤ 0.05). The best efficiency in propionic acid, decrease in methane, and increase in biohydrogenation (79%) occurred with the inclusion of protected PA3 (p ≤ 0.05). In conclusion, supplements with protected PA at 3 and 6% increased PA availability. The results indicate good benefits of protected PA on fermentative variables, and these doses seem ideal for future research in lactating goats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".