Explainable AI analysis for smog rating prediction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Smog poses a direct threat to human health and the environment. Addressing this issue requires understanding how smog is formed. While major contributors include industries, fossil fuels, crop burning, and ammonia from fertilizers, vehicles play a significant role. Individually, a vehicle’s contribution to smog may be small, but collectively, the vast number of vehicles has a substantial impact. Manually assessing the contribution of each vehicle to smog is impractical. However, advancements in machine learning make it possible to quantify this contribution. By creating a dataset with features such as vehicle model, year, fuel consumption (city), and fuel type, a predictive model can classify vehicles based on their smog impact, rating them on a scale from 1 (poor) to 8 (excellent). This study proposes a novel approach using Random Forest and Explainable Boosting Classifier models, along with SMOTE (Synthetic Minority Oversampling Technique), to predict the smog contribution of individual vehicles. The results outperform previous studies, with the proposed model achieving an accuracy of 86%. Key performance metrics include a Mean Squared Error of 0.2269, R-Squared (R 2 ) of 0.9624, Mean Absolute Error of 0.2104, Explained Variance Score of 0.9625, and a Max Error of 4.3500. These results incorporate explainable AI techniques, using both agnostic and specific models, to provide clear and actionable insights. This work represents a significant step forward, as the dataset was last updated only five months ago, underscoring the timeliness and relevance of the research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it