Fuel Consumption Tests for Evaluating the Accuracy and Precision of Truck Engine Electronic Control Modules to Capture Fuel Data
Bibliographic record
Abstract
<div class="htmlview paragraph">Many trucking fleets and organizations are extensively using truck onboard computers (OBC) to gather fuel consumption data from truck engines' Electronic Control Modules (ECM). This study aimed to assess the accuracy and the precision of truck engine control module concerning the fuel consumption data.</div> <div class="htmlview paragraph">The testing methodology evaluated the fuel consumption data provided by the ECM using test track and road fuel consumption tests, short-term operational observation, long-term operational observation and engine dynamometer tests. ECM data were retrieved using either onboard computers (OBC) or engine scan tools. Test track and road tests were mainly intended to evaluate the precision of ECM data for short distances, between 60 and 100 km. More than 220 test runs totalizing 22,000 km were conducted using 23 test vehicles. Short-term operational observation monitored a total of 77 vehicles on 305,000 km and long-term operational observation considered periods between 16 and 43 days, with total distance of 105,000 km</div> <div class="htmlview paragraph">For short distances (less than 100 km), similar engines presented different levels of precision and accuracy. The majority of the tested vehicles showed acceptable precision but for some tests, the accuracy was unsatisfactory. Short-term and long-term operational observations showed satisfactory accuracy and precision. ECM data precision and accuracy vary among engine manufacturers and among engine models from a single manufacturer. ECM accuracy and precision are more reliable when averaging over longer periods or with a larger number of tests or trips. Therefore, it is risky to use ECM fuel data alone for evaluating the impact of a technology or practice. ECM data should always be completed with other available information, such as fleet fuel records. ECM fuel data might be used for long-term operational observations when expected fuel improvements are at least 5%.</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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".