An explainable predictive approach for investigation of greenhouse gas emissions in maritime canada's potato agriculture
Why this work is in the frame
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Bibliographic record
Abstract
• Experimental monitoring of GHG emission drivers of cropping system in Easten Canada • A multi-level eXplainable KNN-GBO scheme to simulation of CO2 and N2O drivers • Applying MCDM-based BSET-WASPAS feature selection to optimize best input • combination • Application of the KNN, Extra-GBO, and RVFL models to validate the main model This study aims to develop an optimized expert system that effectively models and predicts greenhouse gas (GHG) emissions from potato crops, integrating experimental data and advanced computational methods. This research seeks to significantly contribute to mitigating climate change impacts and improving food security. We address the challenges of precise GHG data collection in Maritime Canada's potato cropping system by utilizing a high-precision LI-COR instrument, ensuring accurate and reliable measurements for this study. In this effort, the first stage of the investigation comprised measuring experimental soil properties and greenhouse gas (GHG) emissions, specifically carbon dioxide (CO 2 ) and nitrous oxide (N 2 O) in the potato cropping system, from two fields on Prince Edward Island, Canada. A novel interpretable glass-box intelligent framework was designed in the second part. This approach includes the best subset extra trees (BSET) feature selection, weighted aggregated sum product assessment (WASPAS), gradient-based optimization (GBO) algorithm, and k-nearest neighbours (KNN). The BSET-WASPAS feature selection method first attained four most appropriate input combinations for each target among existing seven input features. Afterwards, the optimal combinations were utilized to feed the KNN-GBO model. Additionally, three comparative machine learning (ML) approaches were considered to validate the main framework: leveraging the extra trees and GBO (Extra-GBO), classical KNN and Random Vector Functional Link (RVFL). The SHapley Additive Explanations (SHAP) tool was employed in the last phase to determine the contribution of features in the primary model. The WASPAS is used to individualize statistical metrics like correlation coefficient (R), root mean squared error (RMSE), and Reliability, aiming for easier and superior model identification. In monitoring CO 2 , the KNN-GBO|Combo 2, owing to its exceptional performance in terms of R = 0.9940, RMSE = 0.2426, Reliability = 97.2973, and WASPAS = 3.39E-6, outperformed the KNN, Extra-GBO, and RVFL, respectively. Moreover, in the N2O scenario, KNN-GBO|Combo 3 regarding (R = 0.9910, RMSE = 0.0940, Reliability = 91.7632, and WASPAS = 4.93E-6) resulted in the most promising performance compared to the KNN, Extra-GBO, and RVFL.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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