Bibliographic record
Abstract
The article highlights the features of innovative technologies of restaurant management. An analysis of the number of restaurants in Ukraine for 2015-2019. The structure of restaurant establishments of Ukraine is given: the share of cafes during the analyzed period decreased by 0.39%, and in 2019 amounted to 37.61%; about a quarter of all establishments are "restaurants", their share increased in five years by 2.26%, and in 2019 amounted to 27.65%; the share of canteens and food delivery establishments decreased by 2.72% over the same period and amounted to 15.12% in 2019; there is an increase in the share of establishments such as "bar" in the overall structure of restaurants, their share increased by 0.85% and is 19.62%. Emphasis is placed on the fact that, wanting to survive in the competition, all types of restaurants are not only looking for new market segments, but also widely use modern innovative technologies and trends in management. The basic principles of innovative management of restaurant economy are allocated: preventive measures; resource optimization; the principle of integration; digital traceability (transparency); responsibility. Promising innovative technologies used in the management of the restaurant business - smart energy; waste management; smart music systems; branded restaurant application; radio frequency identification; QR codes; division of the check in the form of a mobile application; use of robots to automate the main work processes directly when interacting with guests in the restaurant; interactive bar; online ordering. Positive changes in the activities of restaurants from the introduction of innovative management technologies have been formed.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".