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Record W7033736051

Restaurant business

2020· other· en· W7033736051 on OpenAlexaboutno aff

Bibliographic record

VenueElectronic archive of KNUTD (Kyiv National University of Technologies and Design) · 2020
Typeother
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyBusiness EnglishValue (mathematics)Latin AmericansEnglish as a second language
DOInot available

Abstract

fetched live from OpenAlex

This textbook "Restaurant business" is the most complete specialized textbook for training specialists in English in the restaurant business. It is based on the latest data on the main directions of the restaurant industry development. In this we see the value and timeliness of this tutorial that will help on the one hand – to improve their English language skills, and on the other hand – to make better their knowledge in the professional field. The structure of this tutorial is – seven chapters: the history of the restaurant business, restaurant service, food and meals, business and culinary, diets, cookery art, European cuisine (Russian, Ukrainian, English), the American food industry (culinary business in North America and Canada, Latin America), African and Asian cuisine. In the book there is a large number of authentic texts in English, designed exercises, dialogues, charts, coloured inserts. This tutorial allows you to learn the professional vocabulary and improve their English language skills quickly and easily.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.510
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.274
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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