An innovative method for creating and using nanoparticles for gas extraction from gas hydrates
Bibliographic record
Abstract
The growth of prices for traditional energy sources prompts Ukraine to seek new approaches to solving energy problems. Today, the country has intensified its work in this direction, in particular, legislative support is being developed and improved, and the investment climate for alternative energy projects is improving. In many countries of the world, it has long been understood how serious and necessary is the development of alternative energy. At present, in the face of various gas contradictions and unstable oil prices, the need for energy carriers is constantly increasing, which makes it necessary to seek the latest solutions to the energy problem. Many leading countries in the world are engaged in the search for alternative sources of energy, one of which is natural gas hydrates. This relatively new resource offers great opportunities both for economic growth and stability of states, and for the development of scientific institutions in this field. Flagships in the study and development of gas-hydrated deposits are the United States, China, Japan and Canada. Along with them should be noted the achievements of scientists in India, EU countries, Ukraine, Russia and Bulgaria.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".