Zero emissions in the production of hydrogen fuel using seawater as the main resource through the artificial leaf tool: a proposal for a bibliographic review
Bibliographic record
Abstract
Polluted air creates health problems for people, plants and animals today due to many factors in industrial cities and power generation projects, transportation and chemical industry and others. It is for this reason that this research in bibliographic review allows us to know the different solutions to produce hydrogen through the analysis of the Scopus database and the VOSviewer tool that allows us to analyze the data, considering the variables that are artificial leaf, hydrogen, production , clean energy through seawater, graphs and tables were obtained which provide us with an analysis of the number of publications, the countries that carry out these investigations and the bibliometric maps worldwide for a global analysis. The results allow us to analyze and learn about the different solutions and materials that are used to carry out artificial photosynthesis that develops the production of hydrogen by separating water molecules with the aim of emitting zero emissions and being able to use it in different applications such as fuel, energy electrical, industrial uses and others. The purpose of this research is to allow us to make better decisions to apply this methodology according to the materials that we have in greater scope and that is a promising future for a generation of the new industry for the following years, also considering the objectives of sustainable development and finally, motivate readers to continue with these investigations and be able to apply it with institutions in charge of combating this problem.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".