Measurement of the Particulate Matter Emission from Internal Combustion Engines Using an Automatic Method
Bibliographic record
Abstract
The aim of this study was to evaluate the accuracy and precision of measurements of an automatic weighing system used to assess the mass emission of particulate matter emitted by internal combustion engines. Thirty test cycles were carried out for cars equipped with spark-ignition and compression-ignition engines that met the Euro 4, Euro 5 and Euro 6 emission standards. Exhaust gas samples for analysis were taken according to EU 2017/1151 recommendations for driving cycles performed on AVL and Zöllner chassis dynamometers, AVL-CVS i60 LD LE and HORIBA-CVS 7400 S exhaust-gas collection systems, WLTC driving cycle according to EU 2017/1151, NEDC according to UNECE No. 83, RDE, RTS and TFL cycles, non-standard cycles of car manufacturers. The mass emission of particulate matter was measured using Teflon-coated glass filters of Pallflex® Emfab™ type TX40HI20WW, which ranged between 96 ÷ 102 mg. The mass of the filters was measured with a manual method using an MSE2.7S-000-DF scale manufactured by Sartorius and an automatic method using an UMA 2.4Y.F system manufactured by Radwag Wagi Elektroniczne. The mass measurement precision of the manual system for the mass standard was 0.0003 mg, and for the reference filter 0.0034 mg. The precision of the automatic system was 0.0004 mg and 0.0002 mg, respectively. For the reference filter, the measurement precision of the automatic system was more than 18 times better than that of the manual system. It was found that the mass of particulate matter emitted by internal combustion engines ranged between 0.01 mg ÷ 0.52 mg and that the emission of particulate matter ranged between 0.04 mg/km ÷ 2.46 mg/km. The maximum difference in particulate matter emission obtained with the manual and automatic systems was 0.10 mg/km. It was found that the use of the automatic system significantly influenced the ergonomics of the test process by greatly reducing the time taken to test changes in filter mass.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".