Empirical and Predicted Boreal Forest Carbon Pools Following Stem‐Only Harvesting in Quebec, Canada
Bibliographic record
Abstract
Core Ideas Total ecosystem carbon recovered to pre‐harvest levels after seven decades. Predicted and observed carbon pools differed most for deadwood and soil. Modified CBM‐CFS3 initialization and default decay parameters may improve accuracy. Climate change and global wood products demand raise concerns about boreal forest ecosystem resilience to natural disturbances and harvesting. A chronosequence experiment located in Forêt Montmorency, Quebec was used to evaluate the effects of harvesting on carbon (C) recovery trajectories at the stand level over a period of 77 years in balsam fir‐white birch stands. Empirical estimates of 19 Forêt Montmorency forest carbon pools were compared with those simulated by the Carbon Budget Model (CBM‐CFS3) to test model assumptions for predicting carbon dynamics in this forest. The model was initialized using forest inventory data, spatially explicit environmental conditions, and disturbance matrices designed to represent historical spruce budworm epidemics in Forêt Montmorency. Over the chronosequence, total ecosystem C increased significantly ( p = 0.05) following harvest from 211 Mg in year‐zero to 279 ± 8 Mg C ha −1 in year 67 (mean ± SE), suggesting that FM carbon pools were recovering to pre‐harvest levels after seven decades. The CBM‐CFS3 model predicted total ecosystem C stocks within 10% of the empirical mean at stand maturity; however, several predicted C pools deviated from field observations in both C amounts and trends over time. The greatest differences were in deadwood and soil C pools, suggesting that model initialization of dead organic matter pools did not adequately simulate the 1000‐yr history of C‐pool transfers and stand dynamics leading up to the harvest. Modifications to CBM‐CFS3 initialization assumptions and default decay parameters may more accurately simulate long‐term effects of natural disturbances on C pools for this forest region.
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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 | 0.000 |
| 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".