Bibliographic record
Abstract
In Japanese fishery industry, there are some fatal problems to have to improve such environmental problems as global warming and changes in ecosystem or such the price rising of fuel oils as pressing fishery management. If the global warming progresses, the effects of disturbing ecosystem and environmental impact on the marine living things may increase. The exhaust of carbon dioxide that might be induced by the economic human activity brings greatly global warming. One factor of global warming is bringing about by burning the fossil fuel in transportations of vehicles or vessels, etc. The fossil oils of about 40 percent have been utilized for transportations, and then it requires immediately reducing the emission of green house gases. Also, the development of new fuels alternating to fossil fuels is required to reduce the emission of carbon dioxide and to correspond to the rise of a rapid price of fossil fuel. Now, hydrogen gas is the one from regenerating and recycling energy resource and also it may be a ultimate clean energy, because it emits only vapor water when it generates energy, and it is generated by electroanalysis using natural energy such as wind power or photovoltaic energy, etc. In this paper, it was researched worldwide clean energy projects demonstrating in Japan, EU countries, USA, Canada, etc. and they were summarized. Furthermore, it was discussed in the background of fishing boats with hydrogen combustion engine and the approaching. Conclusively, it was suggested that the size suitable for the fishing boat with hydrogen engine was the type of coast fishing boat under 5 gross tons.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".