Effects of dietary additives identified as potential methane mitigators on production characteristics, wool quality and yield and tissue fatty acid composition of sheep
Bibliographic record
Abstract
Effects of dietary additives identified as potential methane mitigators on production characteristics, wool quality and yield and tissue fatty acid composition of sheep The ability of dietary strategies to mitigate CH4 emissions must be balanced with their effects on animal performance in order to be widely adopted by producers. This thesis investigated promising supplements [P. freudenreichii, crude glycerin and two species of micro-algae (A. nodosum and Schizochytrium spp.)] in terms of their CH4 mitigation potential, effects on lamb production, fatty acid (FA) profile of lamb; and wool yield and quality characteristics. It is noted that there is a strong consumer push for a healthier FA composition of lamb and as such producers will inevitably shift production to meet consumer demands. Results presented here indicate the potential of P. freudenreichii to reduce CH4, but showed little effects on FA biohydrogenation. Supplementation of crude glycerin successfully replaced wheat in Merino ewe diets; however, no improvements were observed on wool yield or quality. The supplementation of Tasco® (A. Nodosum) did not affect production performance, but failed to favourably alter the FA profile of lamb as compared to other dietary oils. Conversely, DHA-Gold (Schizochytrium spp.) supplementation elicited a favourable shift in the FA profile of lamb through n-3 enrichment of both adipose tissue and skirt muscle. A further molecular investigation into the regulation of adipogenesis in lambs revealed differences in miRNA expression between subcutaneous and perirenal adipose tissues that could be influenced by micro-algae supplementation. As such, the results presented in this thesis suggest that although supplements may have the potential to reduce CH4 emissions, their effects on production may not always be favourable. In the current case, the supplementation of micro-algae (Schizochytrium spp.) proved to be the most effective at positively modifying the FA profile of lamb.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".