Investigating Impacts of Wood Harvest on the Canadian Boreal Forest Carbon Store
Bibliographic record
Abstract
Earth System Models provide important insight into global climate dynamics. These models often require large computational resources to run, inhibiting accessibility and exploration of a wide range of climate-related scenarios. Machine learning can help by creating an emulation of an aspect of an ESM to enable less expensive scenario simulation. I use a Long Short-Term Memory model to emulate forest carbon dynamics in the Community Earth System Model 2 in order to understand the impact of wood harvest on carbon stocks in the Canadian Boreal forest. To validate the emulation, I use available external datasets that explicitly quantify carbon stocks in soil and above-ground biomass. The emulation can predict CESM2 several carbon stock variables accurately (0.89 R$^2$ Score) and can be explained with important climatic relationships. I then create land-cover scenarios to simulate no wood harvest for the years 1984-2019. These scenarios show that 584 Mt C were lost to wood harvest over this period, with an additional 172 Mt C attributed to regrowth from wood harvest over the same period. The LSTM model I use in this study provides a more flexible approach to investigating land-use change impacts on carbon stocks by harnessing the power of both machine learning models and process-based ESMs. This approach can help understand land-use change scenarios that are not considered in large inter-model comparison efforts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".