CO2 emissions prediction based on regression, neural network and SVM
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
As a matter of fact, with the fast-pace development of global economics and technology, the natural environment is suffering from great amount of greenhouse gases emissions, which attract a lot of attentions from researchers. Specifically, in statistics and data science, experts believe that making accurate CO2 emissions prediction could help governments make policies accordingly. In this paper, three different machine learning models (regression, neural network and support vector machine) are analysed in terms of their construction process and performance on CO2 emissions prediction. Besides, some practical applications from these studies are shown. In general, based on the analysis, these models have made great achievement on CO2 emissions prediction and they all solve the issue in various perspectives. Therefore, this study will show the effectivity of machine learning models on CO2 emissions prediction and encourage more scientists from different majors to take part in it. Overall, these results shed light on guiding further exploration of carbon emission prediction.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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