Bibliographic record
Abstract
The Food and Beverage Department in a hotel is one of the departments that generates big revenue. Kitchen, as food production facility plays a big role in producing these big numbers. Cooperation with related departments in providing food service is very essential. Specially Sales and Marketing Department in selling the food product. Cost Control Department also plays a big role in determining the revenue generated. When the food cost increases (is fluctuating), this is not only the Kitchen's mistake. Because as a team, together all departments should be supporting each other in many ways, especially in sharing crucial information. With this research the writer try to investigate why the communication between departments is very essential in generating maximum revenue for the management, and how the existence of the food cost calculation will influence the food cost percentage, which finally will end up in company's revenue. The research question was:'Is there a relation between the fluctuation of the food cost percentage in Golden Tulip Epe and the implementation of the standard calculation formula? From the literature research there appeared to be seven areas of focus concerning food cost: essential of control svstem(the importance of control system for Golden Tulip Epe);the method of calculation food cost; the method of food cost control ;food control check list; standard recipes; standard portion size; and management responsibilities. Historical research, literature research, observational method and analyzing qualitative data were used as the research methodology. Finalizing conclusions were about traditional kitchen management, non actual food cost report, lack of cooperation and communication between department shortage of purchasing order procedures, lack of work efficiency. The goal of this research is to find out indications of what factor plays a main role in the unacceptable food cost fluctuation at the Golden Tulip Epe and what can be done to improve the management performance.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".