Bibliographic record
Abstract
Based on an analysis of national statistics on the structure and condition fixed production assets of Russiancompanies identified disparities in the structure of the fixed assets in Russia, as well as a high degree of wear andtear, (about 50%). In the study, the reasons for the poor state of basic production assets and identify opportunitiesto overcome the problem of low investment activity of domestic enterprises in the modernization of productionis determined that one of the main constraints is the current tax system and the associated high cost of investment.Determined that under the tax laws do not give preferences to the expected positive effect, because it does nothave a significant impact on reducing the cost of acquisition of machinery and equipment due to the highproportion of it indirect taxes (VAT, duties), which reduces the investment business opportunities at the sametime holding back the development of domestic engineering and construction industry, which are created and thebasic production assets (active and passive parts of them). As stimulus measures proposed substantial cuts inVAT and duties, and in high-tech equipment in strategic industries - complete their cancellation. Dropping out inconnection with this budget revenues at the initial stage may compensate by increasing excise taxes on certainproducts that are detrimental to public health (alcohol and tobacco), increased taxation on the export of capital.As a non-tax instruments to promote development of the proposed technical outsourcing (after-sales service ofprocess equipment), the use of which will provide the companies who invest in technological renovation ofproduction, significant cost savings by reducing the costs associated with the content in the state of highlyspecialized staff and simplify the process of enterprise management . The enterprises of mechanical engineeringwill be able to sustainable development and establishing long-term business relationships with client enterprises.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".