Modelling and optimization of exhaust pollutants and the properties and characteristics of ethanol‐diesel through a statistical approach
Bibliographic record
Abstract
Abstract This work assesses the influence of diesel additives on chemico‐physical properties (flash point, density, viscosity, and cetane number) and exhaust emissions (CO, CO2, nitrogen oxides, and soot). Experimental tests are performed in a 4‐cylinder turbocharged, common rail direct injection engine. Experiments are conducted in different percentages of ethanol (5 and 10 %) and nitrogenate additives (2 and 5 %). The current essay applies various types of tertiary additives (nitro methane, nitro ethane, and 2‐methoxy ethyl ether). All the compositions of ethanol in the diesel make the cetane index, flash point, density, and viscosity reduce. In contrast, these properties get enhanced by adding tertiary additives. Experimental studies demonstrate that nitro ethane is more effective on the decrease of soot emissions and the increase of CO, CO2, and nitrogen oxides.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".