Monitoring of greenhouse gas emission drivers in Atlantic Canadian Potato production: A robust explainable intelligent glass-box
Bibliographic record
Abstract
• Experimental monitoring of GHG emissions in cropping systems of Atlantic Canada. • An explainable GBDT-RUN glass box system to simulate CO 2 , N 2 O, and H 2 O drivers • Incorporating Boruta-GBDT, BSLR, and WASPAS to optimize superior combinations • Validating the main model using LightGBM-RUN and GBDT models coupled with SHAP In this research, a novel explainable multi-level ensemble learning framework has been developed to accurately monitor the greenhouse gas (GHG) emission drivers of the Maritime potato crop system i.e., Carbon dioxide (CO 2 ), nitrous oxide (N 2 O), and water vapour (H 2 O). For this purpose, alongside the GHG emission drivers, the hydro-meteorological and soil properties information was collected from three Canadian sites, two in Prince Edward Island (PEI) and one in New Brunswick. This advanced framework includes a transparent multi-level pre-processing module and a Runge-Kutta optimizer (RUN), integrated with an eXplainable gradient-boosted decision Tree (GBDT) machine learning (ML) technique. The preprocessing scheme meticulously selects the most effective input combinations from the hydro-meteorological and soil properties datasets using hybridization of Boruta-GBDT for feature selection, Best Subset Least Absolute Shrinkage and Selection Operator (BSLR), and Weighted Aggregated Sum Product Assessment (WASPAS). The optimal combinations were then analyzed using the GBDT-RUN and compared against two algorithms: LightGBM coupled with RUN optimizer (LightGBM-RUN) and classical GBDT. The explainability of the primary model was enhanced using SHapley Additive exPlanations (SHAP). Model validation employed various metrics, such as the correlation coefficient (R), squared deviation (SquD), and a range of sophisticated statistical graphics. Results demonstrated that the GBDT-RUN model exhibited superior performance in monitoring GHG emissions (CO 2 |R=0.8431, SquD=17.1759, WASPAS=1.88E-07; N 2 O|R=0.8431, SquD=17.1759, WASPAS=1.88E-07; H 2 O| R=0.8431, SquD=17.1759, WASPAS=1.88E-07), outperforming both LightGBM-RUN and classical GBDT. Furthermore, the explainability analysis identified dew point and soil temperature as the most influential factors in the CO 2 , N 2 O, and H 2 O emissions scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".