Toward a Machine Learning Approach to Predict the CO<sub>2</sub> Rating of Fuel-Consuming Vehicles in Canada
Bibliographic record
Abstract
Global warming is becoming a major concern for almost all countries nowadays. Burning fossil fuels, most of which by vehicles is the main cause of increasing toxic CO2in the air we breathe. Along with other factors, the emission of CO2from vehicles in the environment plays a major role in raising the temperature worldwide. Infrared energy of sunlight by the earth’s surface is absorbed by CO2and then re-emitted in all directions causing the greenhouse effect. So, it is very important to take necessary actions to identify high as well as low CO2emitting vehicles. The main aim of this work is to predict the CO2rating of the vehicle based on various key factors and identify the vehicle with CO2emissions beyond the ideal range. For that purpose, the last five years (2017-2021) fuel consumption rating dataset of Canada which is publicly available has been collected and analyzed first. Then, eight machine learning techniques were applied to predict the CO2rating based on the key features. From our investigation, we have found that the highest accuracy of 96% is achieved by the random forest algorithm. We hope that this work will contribute to the field of identifying and designing low CO2-emitting vehicles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".