346 Feeding Hempseed Cake Alters the Bovine gut, Respiratory and Reproductive Microbiota
Bibliographic record
Abstract
Abstract A growing number of studies have investigated the viability of feeding hemp by-products as livestock feedstuffs; however, their impact on livestock microbiomes remains unexplored. Here, we evaluated the effects of feeding hempseed cake (HSC) on the gastrointestinal, respiratory, and reproductive microbiota in beef heifers. Angus-crossbred heifers [19-months old; initial body weight (BW) = 494 ± 10 kg] were fed a corn-based finishing diet (10% forage) containing either 20% HSC (n = 15) or 20% corn distillers grains (Control, n = 16) for 111 days until slaughter. Individual feed intake, feeding behavior and BW were measured throughout the study. Rumen fluid and deep nasopharyngeal swabs (days 0 7, 42, 70 and 98), and vaginal and uterine swabs (at slaughter) were collected, and the microbiota assessed using 16S rRNA gene sequencing. HSC feeding resulted in reduced average daily gain (P = 0.05) without influencing feed intake and feeding behavior (P > 0.05) (reported elsewhere). Sampling time had a significant effect on both ruminal (PERMANOVA: R2 = 0.39; P < 0.001) and nasopharyngeal (R2 = 0.18; P < 0.001) microbial community structure. There was also a significant effect of diet on the ruminal (d7– 98; 0.06 ≤ R2 ≤ 0.12; P < 0.05), nasopharyngeal (d 42; R2= 0.18; P < 0.001), and vaginal (R2 = 0.06; P < 0.01) microbiota. Although microbial richness in the rumen, nasopharynx, vagina, and uterus was not affected (P > 0.05) by HSC feeding, microbial diversity (Shannon diversity) was increased in the rumen (d42-98) but reduced in the uterus of HSC heifers (P < 0.05). The relative abundance of five ruminal genera was enriched, while five vaginal genera were reduced in HSC heifers (P < 0.05). Overall, the results of our longitudinal study suggest that feeding hemp by-products can alter the bovine gut, respiratory and reproductive microbiota.
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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 | 0.001 | 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".