Evaluation of Biodiesel Blends in a Single-Cylinder Medium-Speed Diesel Engine
Bibliographic record
Abstract
Biodiesel blends were prepared by mixing low sulphur #2 diesel and biodiesel of two origins (canola and frying oil) at two different concentrations (5% and 20%). They were tested in a single-cylinder four-stroke medium-speed diesel engine under three engine modes representing idle, about 50% power and full load conditions. Engine performance and emissions data obtained with the blends were compared to that of engine running with the #2 diesel. Results indicated that the 5% blends could maintain engine power and fuel economy. Frying oil based B5 provided more significant reductions on CO, THC and PM emissions and increments on NOx emissions as compared with that of the canola B5 fuel. The 20% blends reduce engine CO, PM and smoke emissions, but increase NOx emissions by up to approximately 8%. Engine cylinder pressure and injection pressure data was also collected to provide additional information for evaluation of fuel economy and emissions benefits of using the blends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".