Modeling the Process of Forming the Safety Potential of Engineering Enterprises
Bibliographic record
Abstract
The main purpose of the study is to form a methodological approach to modeling the process of forming a safety potential for engineering enterprises.The main method used was the method of multi-criteria assessment of alternatives and the matrix of paired comparisons.An assessment was made of alternative options for ensuring the security of the potential of engineering enterprises for different resource requirements.The study has certain limitations related to the fact that the data and enterprises that were used relate to the engineering ones of Eastern Europe.The results of calculating a rational option for ensuring the safety potential of engineering enterprises for different needs in material, financial, personnel and organizational resources by the method of multi-criteria assessment of alternatives can be used in the future to improve monitoring in practice.The main issues considered during the study were to determine the security potential of the engineering company, to determine the resources needed to ensure the security potential.This methodology allows through comparison of certain indicators or indicators, to determine which management decision is most appropriate to the existing situation.The value of the study lies in the formation of a scientific and practical approach to the formation of the safety potential of engineering enterprises, the use of which, in contrast to the existing ones, is based on the use of methods for multi-criteria assessment of alternatives and a matrix of paired comparisons for the advantage of options.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".