Neat Biodiesel Fuel Engine Tests and Preliminary Modelling
Bibliographic record
Abstract
Engine performance and emission comparisons were made between the use of 100% soy, Canola and yellow grease derived biodiesel fuels and an ultra-low sulphur diesel fuel in the oxygen deficient regions, i.e. full or high load engine operations. Exhaust gas recirculation (EGR) was extensively applied to initiate low temperature combustion. An intake throttling valve was implemented to increase the differential pressure between the intake and exhaust in order to increase and enhance the EGR. The intake temperature, pressure, and EGR levels were modulated to improve the engine fuel efficiency and exhaust emissions. Furthermore, a preliminary ignition delay correlation under the influence of EGR was developed. Preliminary low temperature combustion modelling of the biodiesel and diesel fuels was also conducted. The research intends to achieve simultaneous reductions of nitrogen oxides and soot emissions in modern production diesel engines when biodiesel is applied.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".