Machine Learning Models for Climate Prediction and Adaptive Planning in Ghana: An Integrated Approach
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
Climate change poses significant challenges to agriculture, water resources management, and urban planning in Ghana. Accurate climate predictions are essential for adaptive planning and mitigation strategies. A hybrid ensemble model combining Random Forest (RF) and Extreme Gradient Boosting (XGBoost) was employed. Model performance was evaluated using Mean Absolute Error (MAE) with a 95% confidence interval as uncertainty quantification. RF-XGBoost outperformed baseline models, achieving an MAE of 2.3°C compared to the RF model's 2.8°C and XGBoost's 2.6°C, indicating improved predictive accuracy in climate forecasting for Ghana. The hybrid ensemble approach demonstrated enhanced robustness and precision in climate predictions, facilitating more informed adaptive planning efforts in Ghana. Future research should focus on integrating additional datasets to further refine the models' performance and explore their application across different regions of Ghana. Machine Learning, Climate Prediction, Ensemble Models, Extreme Gradient Boosting, Random Forest Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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