Bibliographic record
Abstract
Biodiesel is an alternative for fossil diesel that is produced by transesterification of oils and fats with alcohol. For the sustainable development of this environmental-friendly fuel, feedstock availability is one of the most important issues. Various oils and fats commonly available in China are investigated to clarify their potential as feedstock of biodiesel, in terms of oil yield, characteristics and fatty acid composition. It was found that high potential in feedstock availability can be expected for oils from palm oil. Calculation is made for the amount of waste oils and fats discarded in China. 10 waste oil/fat samples were collected and subjected to the analyses such as acid value, water content, peroxide value, iodine value and fatty acid composition for evaluating as a feedstock of biodiesel. In general, used cooking oil from food service industry and/or households may consist of rapeseed oil and soybean oil according to Chinese dietary habit. China produced 13.74 Mt of waste oil in 2010, including 6.58 Mt of gutter oil, 1.55 Mt of acid oil, and 5.61 Mt of rice bran oil. If all these waste oils and fats were utilized in biodiesel production, nearly 10.84 Mt of biodiesel can be prepared. On the other hand, approximately 146.34 Mt of fossil diesel fuel was on sale annually in China. It was therefore suggested that approximately 7.4% of annual fossil diesel fuel consumption can be replaced by biodiesel derived from wastes. Key words : Waste oil and fat; Biodiesel; Acid value; Fatty acid composition; Characteristics
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".