HYDROGEN POWER AND NOT ONLY: "PRO" AND "CONTRA"
Bibliographic record
Abstract
The increase in global temperature caused by global climate warming is considered. Open data on Ukraine’s energy strategy until 2050, in particular on the production and use of natural gas and hydrogen, are provided. The results of the energy efficiency of the production of primary energy resources are presented, including main renewable energy sources. The relevance of the problem of production and use of hydrogen as a significant renewable source of reducing greenhouse emissions and increasing energy security is presented. The indicators and their critical indicators for the stability and stability of the terrestrial system of the socio-ecological state of the planet Earth are presented — the so-called planetary boundaries of sustainability, it is emphasized that not only climate change, but also the extinction of biodiversity is critical for life. In response to the latter, the main points of the Kunming-Montreal UN Global Framework Program until 2030 in the field of biodiversity are outlined, the main goal of which is to return the biosphere to recovery. Based on this year’s materials of the International Energy Agency, we will generally consider the global state of hydrogen issues from the point of view of the strategy for the development of production, distribution and final use of renewable hydrogen. Some environmental and energy characteristics of electrolytic hydrogen production are presented. Recommendations for the prospects of hydrogen use are given. Bibl. 34, Fig. 7, Tab. 2.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".