Bibliographic record
Abstract
DEVELOPMENT OF LOCAL COMPONENTS FOR FUEL CELL TECHNOLOGY . The limitation of oil and gas, dependence of system using energy besides oil fuel, and also awareness of environmental friendly policy have become urgency of fuel cell research. Alternative energy becomes main priority development in the world. Not only in developed countries such as US, Japan, Europe and Canada, many developing countries in Asia are running up to catch the hydrogen used technology. Furthermore, Indonesian policy of energy are shown a requirement to develop on renewable energy and the primary energy mix (Perpres 5/2006, 25 Januari 2006, Kepmen ESDM No. 0983 K/16/MEM/2004, Kepmen ESDM No. 0002/2004, PP No. 03/2005 about change to PP No.89/1989). According to national research strategy, especially of hydrogen and fuel cell technology in 2009, manufacture and production technology, distribution, security of hydrogen and PEMFC technologies is needed. Indeed, in 2025 Indonesia must have national attached PEMFCs 50 kW and capacities goals of 250 MW. On the other hand, vehicle of fuel cell will step into market in the year 2010-2015. The market for mobile fuel cells, with DMFC technologies expected to account for a large portion, is projected to reach US$ 2.6 billion by 2012. Therefore, fuel cell technology is become a crucial technology including of opportunity of local component to contribute in manufacturing fuel cell technology and also a hydrogen storage technology systems. Herein we described the development of various components of fuel cell along with the process such as polyelectrolyte membrane of PEMFC and DMFC, bipolar plate and steam reforming methane with inorganic membrane in BPPT.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.018 |
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".