Research on Evaluation Methods for Particle Emission Levels of Retrofit DPF in Engineering Machinery
Bibliographic record
Abstract
Many local governments currently require diesel-powered engineering machinery to be retrofitted with Diesel Particulate Filters (DPF) to reduce particle emissions. However, some machinery users remove or damage the filter in DPF to reduce maintenance costs, resulting in direct emission of particles into the air in the exhaust gases. This study proposes a method of using portable emission equipment to directly measure exhaust particulate matter to accurately assess whether the DPF is functioning properly. A comparison of the emission characteristics of particulate number concentration under high and low idle conditions was conducted in the study, revealing that measuring PN under high idle conditions can accurately identify whether the DPF in the machinery is functioning normally. At the same time, a comparison was made between the PN test results under high and low idle conditions and the current free acceleration smoke test results. It was found that machinery using electronic control systems cannot use the free acceleration smoke method to identify whether the DPF in the machinery has been damaged.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".