Cotton Oil and Sunflower Oil Fuel Mixtures
Bibliographic record
Abstract
Air pollution is made up of many kind of gases, droplets and particles that reduce the quality of the air. Particles include dust, dirt, soot, smoke and liquid droplets. Some of these particles are large enough to be seen as soot or smoke, while others are so small that can be detected individually with a microscope. Some particles are emitted directly into the air from a variety of sources that are either natural or related to human activity. Those related to human activity include motor vehicle emissions, industrial processes such as electricity generation, incinerators and stone crushing. At this paper will be compared the emissions of pollutants when are used as a fuel the mixtures of diesel-cotton oil and diesel- sunflower oil in a Diesel four-stroke engine. Specifically, the mixtures that have been used are the following: diesel-10% cotton oil, diesel-20% cotton oil, diesel-30% cotton oil, diesel-40% cotton oil, diesel-50% cotton oil, diesel- 10% sunflower oil, diesel- 20% sunflower oil, diesel- 30% sunflower oil, diesel- 40% sunflower oil, diesel- 50% sunflower oil. For those mixtures, it has been measured the emissions of Carbon monoxide (CO), hydrocarbons (HC) and Nitrogen monoxide (NO) and also the fuel consumption. Key words: Gas emissions; Cotton oil; Sunflower oil; CO-HC-NO-smoke emissions; Biofuels
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 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".