Analysis and Exploration of Carbon Emission Dataset using Machine Learning Techniques
Bibliographic record
Abstract
One of the primary environmental climate issues in metropolitan cities is that most people are faced with low air quality. Climate variance is impacting the world, triggering drought, storms, and extreme weather events. The primary contributor to climate change is affected greenhouse carbon gas emissions, of which carbon dioxide (CO2) and carbon monoxide (CO) make up the majority. The carbon emissions are expected to rise steadily worldwide. Many factors, including the burning of fossil fuels in transportation and manufacturing sectors, cause climate variance. Fast urbanization has used a high rate of motor vehicles as compared to a rural area. In metropolitan cities across the world, automobiles are the primary cause of air pollution. The use rate of vehicles keeps increasing, and that results in traffic congestion. This work is focusing on collecting two real-time vehicle emission datasets from two different devices to predict and compare the emissions of different light-duty vehicles by using the machine learning techniques. This research also analyzes the Canadian government emission dataset It shows the results of emission by fuel type and vehicle model. It shows the comparison graph of, mean square error, root mean square error and accuracy comparison of different machine learning algorithms. The work suggests some policies for reducing the carbon emissions. Even the government can adapt certain policies for mitigating the carbon emissions of individual vehicles. In this research work issues for reducing the emission of individual vehicles have been addressed. This work helps the automobile sector to reduce the emission and play a part to save the environment.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".