Effects of a wax organogel and alginate gel complex on holy basil (<scp><i>Ocimum sanctum</i></scp>) <i>in vitro</i> ruminal dry matter disappearance and gas production
Bibliographic record
Abstract
BACKGROUND: The objectives of this study were to: (a) select an ideal organogel for the oil phase of a novel gel encapsulation technology, (b) optimize the formulation of an organogel and sodium alginate-based gel complex, and (c) examine the rumen protective ability of the gel by measuring 48-h in vitro ruminal dry matter disappearance and gas production from encapsulated dried and ground holy basil leaves. RESULTS: A rice-bran wax and canola oil organogel was selected for the oil phase of the gel complex as this combination had a 48-h dry matter disappearance of 6%, the lowest of all organogels analyzed. The gel complex was formulated by homogenizing the organogel with a sodium alginate solution to create a low-viscosity oil-in-water emulsion. Average dry matter disappearance of gel-encapsulated holy basil was 19%, compared to 42% for the free, unprotected holy basil. However, gel encapsulation of holy basil stimulated gas production. Specifically, gas production of encapsulated holy basil was four times higher than the treatment with holy basil added on top of the gel prior to incubation rather than encapsulated within the gel. CONCLUSION: Although the gel itself was highly degradable, it is speculated encapsulation thwarted holy basil's antimicrobial activity. © 2018 Society of Chemical Industry.
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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 | 0.000 | 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".