Evaluation of Factors Affecting Vehicle Emission Compliance Using Regional Inspection and Maintenance Program Data
Bibliographic record
Abstract
In-use vehicle regional inspection and maintenance (I/M) programs in the United States (US) and Canada generate a tremendous volume of data that provides a means for evaluating vehicle emissions compliance in actual consumer use. In this study, IM240 test data for several 1996 to 2001 vehicle models are analyzed from different regional programs in the US and Canada to confirm the suitability of using these data for evaluation of vehicles equipped with advanced emission control technology and to examine the various potential factors responsible for emissions noncompliance. Relative comparisons between US and Canadian program data are made for vehicle models used in the Alliance of Automobile Manufacturers (AAM) MMT® Test Program to examine the potential impact of differences in fuel properties on consumer experience and vehicle compliance. The compliance rate during IM240 testing is additionally examined for a number of 2000 and 2001 models that were certified by EPA with emissions levels equal to or less than Tier 2 Bin 5 standards. The totality of these analyses demonstrate the continued compatibility of the gasoline additive MMT® in consumer or in-use vehicles equipped with advanced emission control technologies.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".