Time Resolution Effects on Accuracy of Real-Time NOx Emissions Measurements
Bibliographic record
Abstract
The advanced development of a previous in-use emissions measurement system developed at the University of Alberta, has been used to illustrate the challenges in accurately measuring real-time mass emissions of NOx, with specific attention given to the issue of sensor time resolution. An analysis of the alignment of vehicle and emissions data has shown constant value time shifting of remote emissions sensor data, to match vehicle data, as the most accurate method for synchronization. Although variable time shifting routines theoretically determine alignment time more accurately, the variable shifting of slow response sensor data has shown an added smearing effect to time shifted remote analyzer data. The effect of sensor response time on accuracy of mass emission rates, has shown that slow response remote emissions sensors are under predicting the total emissions produced by vehicles. Using data post processing to correct first order time response characteristics of the NOx sensors, resulted in improved time alignment of NOx spike peaks with mass air flow (MAF) peaks, faster response times, and larger peak concentrations. The response corrected g/km emission rates ranged from negligible change to 2.5 times original uncorrected results, indicating the dramatic range with which signal processing can effect emissions results. These results indicate the importance of sensor time resolution, and the benefits which can be realized when utilizing data post processing to correct slow responding sensors. However, limitations in the form of signal noise amplification, uncertainty in peak concentration values due to slow sampling rates, as well as peak NOx catalytic reduction characteristics, introduce uncertainty into its definitive results.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".