An XGBoost-Based Machine Learning Approach to Simulate Carbon Metrics for Forest Harvest Planning
Bibliographic record
Abstract
It has become increasingly important to incorporate carbon metrics in the forest harvest planning process. The Generic Carbon Budget Model (GCBM) is a well-recognized tool to evaluate the potential impact of management decisions on carbon sequestration and storage, supporting sustainable forest management planning. Although GCBM is effective in carbon budgeting and estimating carbon metrics, its computational complexity makes it difficult to integrate into forest planning with multiple scenarios. In this regard, this study proposes using machine algorithms to expedite the output generated by GCBM. XGBoost was implemented to estimate the carbon pool and NEP in managed forests of Quebec. Furthermore, polynomial regression was also implemented to serve as a validation benchmark. Datasets with total sizes of 13.53 million and 7.56 million samples were compiled for NEP and carbon pool forecasting to run the model. The results indicate that XGBoost was able to accurately replicate the performance of the GCBM model for both NEP forecasting (R2 = 0.883) and carbon pool estimation (R2 = 0.967 for aboveground biomass). Although machine learning approaches are comparatively faster, GCBM still offers better accuracy. Hence, the decision on which method to use, either machine learning or GCBM, should be dictated by the specific objectives and the constraints of the project.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".