Experimental Study of Emissions and Performance of Internal Combustion Engine Fuels
Bibliographic record
Abstract
Experimental study of 8 different internal combustion fuels is conducted. The 8 fuels are: octane 90, octane 95, blend of 10% methanol & 90% octane 90, blend of 10% methanol & 90% octane 95, blend of 20% methanol & 80% octane 90, blend of 20% methanol & 80% octane 95, blend of 30% methanol & 70% octane 90 and blend of 30% methanol & 70% octane 95. A dynamometer setup is used to conduct the experiments and the emissions and torque at different speeds were measured. The efficiency, power and specific fuel consumption were calculated. The experimental results are represented graphically from which the advantages and disadvantages of each fuel relevant to environment and performance are extracted. The best fuels for internal combustion engines are recommended.
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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 | 0.000 | 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) | 0.000 | 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".