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Bibliographic record
Abstract
This article discusses a relatively new potential source of energy, namely, gas hydrates and existing methods\nof their production, both onshore and on the continental shelf. Emphasis is placed on the technologies that are\nsuccessfully used on the main gas hydrate fields of the world, namely: Messoiakhske field of gas hydrates, which is\nlocated in the north of the Western Siberia, Alaska Kuparuk field, Mialik field (Canada), and the accumulation of\nfields in the Nankai basin on the Japan Sea shelf.\nInterest in studying gas hydrates is increasing every year due to the continuous production of such traditional\nhydrocarbon energy sources as oil, gas, and gas condensate. Hydrate reserves on the planet, according to rough\nestimates, comprise at least 250 bln. m3. This is a rather pessimistic estimation, but even it exceeds the known\nreserves of conventional natural gas that are equal to 187,1 bln. m3 in accordance with the current data of the BP\nStatistical Review. Approximately 98% of the world gas hydrate reserves are concentrated in the ocean, and 2% are\naccumulated on land in permafrost. The available theoretical developments of Ukrainian scientists and actual data obtained in the study of gas\nhydrates of the Black Sea, confirm the value of comprehensive research in order to introduce technology of methane\npractical extraction from the Black Sea hydrates for the needs of the Ukrainian economy.\nThis article presents the basic methods of extraction of natural gas hydrates, which can be successfully used on\nthe fields of the Black Sea. Such methods include the method of hot water circulation in the well bore, method of\nheating the well bore by heaters, cyclic-steam stimulation treatment, thermal flooding, gradual dissolution of the top\nlayer of gas hydrate with water, reduction of the hydrostatic pressure etc.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".