Introduction of Internal Audit as an Innovative Tool for Improving the Economic Efficiency of Enterprises
Bibliographic record
Abstract
The production of paint and varnish materials is one of the main sectors of the chemical industry, which is rapidly and dynamically developing in the context of innovative changes.Enterprises of the paint and varnish industry create new jobs using the latest technologies, including digital ones, which can be implemented on a powerful material and technical base.The study examines global trends in the development of paint and varnish industry enterprises and determines prospects for major manufacturers.In the article, the impact of the global economic crisis deepened by the COVID-19 pandemic on the production and consumption of paint and varnish materials is analysed.The pandemic has lowered prices for chemical products, reduced orders for the supply of paint and varnish materials, and considerably increased international competition between manufacturers.Moreover, the study estimates the volumes and substantiates the need for investment in the further technological development of paint and varnish industry enterprises to reduce the energy intensity of production, material consumption of products and ensure their high quality, affordable price, and environmental safety.An internal audit of fixed assets at paint and varnish industry enterprises revealed a substantial deviation in the cost of fixed assets in the financial statements (it can reach 10-14%).Timely and well-founded management decisions on the reproduction and modernisation of fixed assets will provide enterprises with the opportunity to use the latest technological support for the production of quality and environmentally friendly products, increase their economic efficiency and competitiveness.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".