Potential uses for solid biofuels from non-food crops
Bibliographic record
Abstract
The Directive 2009/28/EC on the promotion of energy from renewable sources (RES), sets mandatory national targets so as to be able to achieve a 20% share of energy from renewable sources in gross final energy consumption in the Community in 2020, the aim of the Polish is to achieve by 2020 a 15% share of renewable energy in gross final energy consumption. Thus, use of fossil fuels for energy production should you gradually reduced in favor of renewable energy sources. Usually, however, the change in the method of heating or design of installations using renewable energy sources to incur significant capital costs. In addition, the economic balance during the operation also sometimes detrimental to the modern great human and environmental technologies. Therefore, you should look for low-cost renewable fuels that may be used in particular in those households where there is no possibility of the use of gas or heat delivered from sources of power plants. This paper describes the possibility of using untreated plant biofuels. After the species were taken into account: Canadian goldenrod (Solidago canadensis L.) and mugwort wormwood (Artemisia absinthium L.). These plants are considered weeds have many advantages enabling wider use for energy purposes.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
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".