Bibliographic record
Abstract
The energy sector of Bangladesh is heavily reliant on fossil fuels. Though natural gas and coal are the dominant sources of power production, the country has failed to explore and extract additional gas and coal resources due to a lack of sophisticated infrastructure and poor energy policy. Due to the prior reasons and lack of feasibility as well as the commercial viability of coal, the Government of Bangladesh planned to produce energy using imported Liquid Natural gas (LNG) as a substitute. However, due to the worldwide crisis, LNG prices began to surge sharply by the end of 2021, forcing the government to impose import restrictions and raise fuel and gas prices. As a consequence, at present, Bangladesh is facing an unanticipated energy crisis with growing electricity and gas prices. Bangladesh needs a sufficient, affordable, and environmentally friendly energy supply to mitigate this challenge. In this context, only the adoption of renewable resources in the energy production sector in a vast manner can bring groundbreaking changes to this country. Therefore, it is high time to reduce the number of furnace oil, diesel, and gas-fueled power plants and replace them with ones powered by renewable resources like solar, biogas, wind, and hydro. This paper's major goal is to emphasize the renewable resources that are now used to generate power, as well as the numerous policies and initiatives taken by the government of Bangladesh to promote this industry and exhibit the potential for renewable energy in the future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".