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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".