Evaluation of Raw Yeast Extract (Saccharomyces cerevisiae) as an Ingredient, Additive or Palatability Agent in Wet Diet for Cats
Bibliographic record
Abstract
Three experiments were performed to evaluate the effects of strain-specific yeast extract (SSYE) as an ingredient, functional additive or palatability agent when supplemented in its raw form in wet cat food. SSYE as ingredient – SSYE was chemically characterized and its use evaluated through fourteen cats divided into two treatments: control diet (complete wet adult cat food) and control diet with 30 % replacement by SSYE. The results of apparent digestibility coefficient of SSYE were 71.64 % for dry matter, 72.55 % for organic matter, 50.78 % for ashes, 78.59 % for crude protein, 84.33 % for the energy gross and digestible and metabolizable energy value, respectively, of 4,247 and 4,163 kcal/kg, these results indicated that SSYE is comparable to other protein sources for cat’s food. SSYE as a functional additive - twelve cats were distributed into two 6x6 latin squares (treatments; experimental periods), and the treatments were control diet and replacement levels ranged from 2 % to 10 % SSYE. The following parameters were evaluated: digestibility, energy utilization, nitrogen balance, serum urea and creatinine levels. No differences were found. SSYE as palatability agent – Were used twenty cats by comparing the control diet with 2 % replacement by SSYE. A significant difference (P < 0.01) was observed with a preference for control diet. SSYE is a potential protein source for cats; however, it is not effective as additive and may compromise palatability when supplemented in its raw form in complete wet cat food.
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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".