Application of flow methods for material and financial resources management to forecast oil production in Russia
Bibliographic record
Abstract
© 2014 Canadian Center of Science and Education. All rights reserved. The paper considers the impact of flow methods on long-term development of oil extracting industry, the impact being associated with mineral resources production tax variation. Six oil production scenarios for Russia have been considered, comparative analysis of these scenarios is presented. By the end of the calculation period, the scenario that provides for 5% decrease of tax burden closely approximates the scenario of oil production under effective taxation system In terms of budget receipts volumes. The scenario with mineral resources production tax rate increase is the worst in terms of oil industry growth leading to the industry collapse. According to this scenario, an operating company’s tax burden increases to 78%. So far, the world practice has not witnessed economic growth under conditions of taxes as high as this; furthermore, taxation history testifies that too high taxes have not been paid. In terms of oil industry growth, the scenario that provides for 5% decrease of tax burden is the most credible and promising, provided the released flows are invested in production. The same level of tax burden decrease (5%) is required to attain oil production levels in Russia in 2018-2020s according to the 2030 Development Strategy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".