Stack-ClimaBoost: A Model for Analysing the Patterns of Global Warming Across the Continents
Bibliographic record
Abstract
The increasing severity of climate change, including global warming, makes it crucial to act quickly to adapt and reduce its impact.To address the complex issues of climate change, we need to understand how it affects different continents.Traditional methods of predicting climate change often fail due to the inherent complexities and nonlinearities of climate systems.Thus, to overcome these limitations, this study proposes a machine learning-based stacking model.Initially, the state of art models was trained while the results are unsatisfactory, the grid search optimization was employed to improve the results.However, the results produced were in a mediocre state.Thus, a stacking based, Stack-ClimaBoost model was proposed.This model optimizes the integration of Random Forest (RF), CatBoost (CB), and Light Gradient Boosting Machine (LGBM) using grid search optimization.The Stack-ClimaBoost model outperforms previous state-of-the-art models obtaining a low MAPE 0.765, an RMSE of 2.254, and an R 2 value of 0.9003.In addition, for each of the seven continents Stack-ClimaBoost model performed better with a low MAPE (1.8653-5.8280), an RMSE (1.2-4.57), with a higher R² value of (0.65-0.94).With its adaptable solutions, the proposed Stack-ClimaBoost Regressor model could excel in environmental research and climate modeling on multiple continents.Enhancing precision facilitates more dependable prognostications of temperature patterns, thereby supporting proactive strategizing and decision-making aimed at alleviating the consequences of climate change.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".