Ensuring Sustainable Development of the Enterprise During the Transition to Industry 5.0
Bibliographic record
Abstract
The prerequisites for the study were the strengthening of signals for the development of Industry 5.0.The main purpose of the article is to model the sustainable development of the enterprise in the context of the transition to Industry 5.0.To do this, the scientific task will be to select and implement a methodological approach with elements of modeling to ensure sustainable development.The object of the study is the sustainable development of the transportation and travel enterprise.The basis of the methodology is the modern technique of functional modeling of planning and management decision-making.For this, a modern modeling method using graphic language technologies was chosen.The obtained results of the study have elements of novelty through the presented two directions for ensuring sustainable development for a really practicing enterprise.It is determined that the preparation of entrepreneurial and production activities before the introduction of Industry 5.0 is not an easy and fast process, but it is completely justified today.The innovativeness of the results obtained is presented in the form of a sustainable development model.The study has limitations and they consist in using the experience of the previous industrial revolution and exclusively take into account the theoretical vision of Industry 5.0.Further research will include benchmarking to better understand the work of sustainable development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".