Підходи до диференціації величини ставки плати за користування надрами
Bibliographic record
Abstract
Peculiarities of taxation of oil and gas industry enterprises of the countries with developed market economy, in particular Canada, are considered in the article. The techniques of differentiation of tax rates for subsoil use on the basis of production approach are shown. It has been analysed the offers of foreign scholars as for the composition of the factors on the basis of which the differentiation is performed . For each of these factors the coefficient has been set. Within the review of the taxation of domestic oil and gas companies it has been determined the absence of an objective and comprehensive approach to the formation of a fiscal tool. On the basis of a critical analysis of the current mechanism of taxation of enterprises it has been proved the necessity of differentiating of tax rates charges for subsoil use, depending on the factors. Based on the analysis of the mechanisms of taxation of mining companies in foreign countries, it has been proposed the system of measures that can move the emphasis of taxation in the oil sector from fiscal function to regulatory and incentive ones: to make a differentiation of fee rates for the use of mineral resources taking into consideration the differences of hydrocarbon production due to geological characteristics of the deposits; to develop a classifier conditions for achieving of which will be provided with the benefits in the form of a zero rate of payments for subsoil use (the use of new technologies to extend the exploitation, production in marginal wells). The areas for the development of the own methods of differentiation rates of fees for use of mineral resources, by taking into account the following factors: oil water content, stages of development and hydro-conductivity of the layer are established.
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 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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".