Fuel efficiency and exhaust emissions for biodiesel blends in an agricultural tractor.
Bibliographic record
Abstract
Field experiments were conducted for spring tillage and soybean planting a 12 hectare field using four different blends of biodiesel derived from soybean oil, B100, B50, B20 and diesel. An instrumented tractor equipped with a set of sensors and a data logger to monitor and record implement draft, fuel consumption and other tractor operational parameters was used for field work in the experiment. Auxiliary fuel tanks and a system of valves were installed on the tractor to allow switching among premixed blends of biodiesel during the field experiments. An instrumented exhaust pipe was installed on the tractor for measurement of exhaust gas temperature, mass flow, and NOx (nitrogen oxides) emissions. Results showed that B20 had very similar performance with diesel in terms of fuel consumption, fuel efficiency and NOx emission. Higher fuel consumption and lower fuel efficiency were observed for B50 and B100 blends which is due to the lower energy content of the biodiesel. NOx emissions were higher with blends with higher biodiesel contents. CO2 emissions estimated from life cycle analysis were substantially lower for blends with higher biodiesel contents. The tractor was overpowered for the three meter wide grain drill, and this mismatch between the tractor and equipment resulted in lower fuel efficiency, and higher NOx emission on a per hectare basis compared with the tillage implement with a near optimal tractor-implement match.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".