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Record W7145492307

クリ一ンエネルギー利活用の現状と水素エンジン漁船開発の背景

2008· article· ja· W7145492307 on OpenAlexaboutno aff
Satoru Ezoe, Kaito Takahashi

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2008
Typearticle
Languageja
FieldEnergy
TopicEnergy, Environment, Agriculture Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFossil fuelGlobal warmingNatural gasFishingCombustionRenewable energyCarbon dioxideWind powerPetroleum
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0030.002
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.016
GPT teacher head0.225
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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