Bibliographic record
Abstract
<div class="htmlview paragraph">FPInnovations investigated the impact of different diesel/biodiesel blend ratios on engine performance, exhaust emissions, and fuel consumption. A CAT 3406E engine was used for this evaluation. The objective was to give those considering using biodiesel in their operations a reference of how their equipment would perform with different biodiesel blends.</div> <div class="htmlview paragraph">Testing was conducted in January and February 2008 in Vancouver, B.C. at the British Columbia Institute of Technology, Heavy Equipment Group Campus. The following blend levels were tested: 100% diesel (typical winter diesel for the local area), B10, B20, B30, B40, B50, and B100. The biodiesel fuel used for this study met ASTM D6751 specifications and was made from virgin canola feedstock provided by Milligan Bio-Tech of Foam Lake, Sask. An engine dynamometer was used to run the engine through a series of duty cycles with the different fuel blends. A minimum of three runs were required for each of the blends, with the fuel consumption varying no more than 2% between the runs. Exhaust emissions measurements (NOx, CO2, O2, CO, and SO2) were recorded at one-second intervals. This paper reports the findings of this study.</div>
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".