Bibliographic record
Abstract
The problem of the adverse effect of greenhouse gases released in the atmosphere became a global one in the recent years due to the caused probable climate changes. The mostly spread greenhouse gases are methane and carbon dioxide emitted by agriculture (methane), transport, industry and households (carbon dioxide). Carbon dioxide is considered as a big threat for climate changes because of its very powerful emissions all over the world. There are different ways for remedy of this global threat. First, it is to increase the energy efficiency to spend less carbon containing fuels in transport and industry. Another way is to replace, at least partially, the carbon containing fossil fuels by renewable ones, like wind, solar energy and waterpower, or by recyclable biomass. The third one is to recycle the emitted carbon dioxide to fuels (e.g. methane, synthesis gas, light hydrocarbons) and/or useful chemical products (methanol, formic acid, etc.). Mostly the carbon dioxide recycling is based on endothermic processes requiring input of energy thus polluting atmosphere with carbon dioxide in the general case. That is why a carbon-free sources of energy must be applied. Fuel cell applications seem promising to such a purpose.This minireview presents a comparison of the available data for reverse carbon dioxide conversion to methane and organic compounds.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".