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Bibliographic record
Abstract
Ukraine being the fifth country in the world by the total length of pipelines after the USA, Russia, Canada and\nChina (gas â 33.327 thousand km, oil â 4.514 thousand km, petroleum products â 4.211 thousand km), is supposed\nto get significant positive economical results and geopolitical dividends from efficient usage of this resource.\nHowever, a number of different factors even the potential of the efficient usage of Ukrainian pipelines. For example,\ngas transportation through the territory of Ukraine is provided by more than 750 gas-compressor units with overall\npower of 5.56 million kilowatts, while nearly 82.5% of these units are equipped with gas-turbine engines. More than\n70% of these units actually are worn out and should be changed urgently. Inefficient consumption of fuel gas by\nout-of-date and physically worn out gas-compressor units causes. The resources of over 70% of the units have been\nexhausted and thus the units have to be replaced, as well as the ineffective usage of power gas by dated and physically\nworn-out units causes the growth of expenses of power gas for the technological needs of the gas-transport\nsystem and leads to unjustified financial expenses. That is why the problem of decreasing the power intensity of Ukrainian gas-transport system becomes a high priority task for the process of reconstruction and modernization of\nUkrainian compressor stations.\nThe article analyses the main pros and cons of using gas turbine and electric drives on compressor stations\nof gas-main pipelines, proposes the methods and shows the results of calculations of financial expenses on the\nrealization of various conditions and rates of gas transportation while using gas turbine and electric drives and\nintroduces the notion of the âcoefficient of energy efficiencyâ of drive usage.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.012 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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; both teacher heads agree on what is shown here.
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".