Visualization of Greenhouse Gas Emissions among OECD Countries
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
The increase in temperature compared to the pre-Industrial Revolution levels, the melting of glaciers, the endangering of living species, and climate change have started to spur the parties into action, and efforts have been made to prevent global warming through agreements and protocols. In this study, the global situation of the gases causing greenhouse gas emissions in 2023 of the international community OECD was examined and the similarities and dissimilarities of the member countries were visually revealed using multidimensional scaling analysis, one of the multivariate statistical methods, based on the variables of Carbon dioxide, Methane, Nitrogen dioxide, and HFC134-a. The study, conducted using the Euclidean distance measure, illustrates the similarities and dissimilarities among countries in a three-dimensional space based on the Stress value. The study demonstrates that France, Türkiye, Canada, and Colombia are similar regarding greenhouse gas emissions. On the other hand, Mexico, Canada, Colombia, Germany, France, Poland, Japan, South Korea, and Israel differ from other OECD countries. The United States exhibits considerable dissimilarity with other OECD countries. Moreover, other OECD countries share similar characteristics. In this way, the multidimensional scaling method will contribute to the comparison of similarities among countries and, in this context, to the development of policy recommendations.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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