Emissions Testing From the Use of Various Biodiesel Blends in Representative Canadian Army Equipment
Bibliographic record
Abstract
A comprehensive series of emissions measurements were taken from two pieces of equipment used by the Canadian Forces. A field kitchen burner unit and the engine from the Light Armoured Vehicle III (LAVIII) were operated using two base fuels: low and ultra low sulphur diesel fuel blended with three different biofuels. Methylesters from canola, tallow and yellow grease were mixed in a range of volumetric proportions from 0 to 20%. Additionally, both very low sulphur diesel and aviation turbine fuel (JP-8) were tested against neat low sulphur diesel fuel. The complete chemical analysis conducted on all test fuels will not be presented here. A full range of gaseous emission measurements were obtained including oxides of nitrogen, unburnt hydrocarbons, carbon monoxide and dioxide and limited sulphur dioxide measurements. Two means of monitoring particulate matter were used with one proving to be more effective than the other. Novel techniques were used to obtain the burner unit emissions results and the AVL 8-mode test sequence was applied to the Caterpillar engine from the LAVIII. Although emission trends were detected, levels were often on the margins of perceptibility of the gas analysis system, and atmospheric conditions were challenging. Nevertheless, a methodology was developed and refined. Some correlations were made between the chemical analysis and emissions results. The testing will allow the Canadian Army to estimate its emissions footprint.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".