Research on the Relationship between Increasing Carbon Dioxide Concentration and Extreme Weather Events
Bibliographic record
Abstract
Abstract—Carbon dioxide is the main greenhouse gas emitted through human activities that impact human beings and the natural ecosystem. According to a study done in 2019, human activities were seen to accumulate approximately 80 percent of all United States greenhouse gas emissions. The earth's carbon cycle depicts carbon dioxide as a naturally present gas circulating in plants, soil, ocean, animals, and the atmosphere. Therefore, the purpose of the study is to assess how an increase in the concentration of CO2 is correlated to more extreme weather events. The research was geared to help combat climate change since the impacts of severe weather are always catastrophic. Also, the study would help in improving public health, avoiding the runaway cost of climate change, protecting vital ecosystems and species, and preserving water resources and clean water. The research topic is carbon dioxide's role in extreme weather events. Literature studies were used in collecting qualitative data. From the analysis, the study found out that an increase in carbon dioxide concentration leads to more extreme weather and climate events in the atmosphere. It discovered that human-related emissions since the industrial revolution are responsible for an increase in carbon dioxide.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".