An Investigation of Canadian Greenhouse Climate Prediction using Time Series
Bibliographic record
Abstract
Due to the increasing global warming trend, the greenhouse effect is becoming more and more serious, which caused a very hot summer around the world. This study aims to identify the most important factors contributing to global warming by investigating and predicting greenhouse gas (GHG) emissions. There are not many studies to predict Canada's future GHG emissions. Therefore, It was decided to use the ARIMA model in a time series analysis to predict and simulate GHG emissions in Canada. A dataset containing seven different aspects of Canadian GHG emissions over the past 27 years was used. The result shows that overall emissions will continue to rise but the growth rate of GHG emissions will decline. Among them, Agriculture and Transportation are the two influencing factors that will increase GHG emissions the most in the future. In general, to reduce GHG emissions in the future, people need to live a low-carbon life and reduce unnecessary means of transportation or use more energy-saving resources such as electric cars. For agriculture, it takes less land to produce more food, such as hybrid rice in China. The fundamental reason is that the earth's population keeps increasing, people need more private cars to travel easily, and more food, thus increasing the area of arable land but reducing the area of green forest.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".