The impact of Russian Energy Resources on the Economic Growth of the EU: Using Computational Intelligence Algorithms
Bibliographic record
Abstract
This study explores the impact of Russian oil and natural gas on the economic growth of the European Union. The Gradient boosting algorithm was relied on to determine this effect because of its high prediction metrics (MSE: 0.002, RMSE: 0.040, MAE: 0.034, R2: 99.9). The study depended on three scenarios. The first scenario is that Russia's exports of both products decline to half the year 2022, then to the quarter of 2023, and this second scenario, then the worst scenario, is to prevent Its exports of both products in 2024. But the result is a decline in the European Union's economic growth in 2022 to (-2.15%), then it turns to 2.85% in 2023, and then to 3.86% in 2024, i.e., in the worst scenario year. The evidence for this is that the economies of these countries reduced their growth rates in 2020 (the Covid-19 crisis) to -5.96%, which turned to positive growth in 2021, amounting to 5.38%. This indicates these economies' ability to adapt in the short term by providing alternatives to the crisis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".