Using the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) to examine the impact of harvest and fire on carbon dynamics in selected forest types of the Canadian Boreal shield
Bibliographic record
Abstract
The objective of the study was to assess the responsiveness of the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) to management scenarios that included three rotation lengths (50, 100 and 250 years) under harvest and fire disturbances in six forest types (poplar deep soil, black spruce deep soil, jack pine deep and shallow soils, hardwood mixedwood and other conifer lowland). Outputs from five carbon (C) pools were considered: merchantable stemwood (stump height of 30 cm, minimum DBH of 9 cm and a minimum top diameter of 7 cm), deadwood, soil C, total ecosystem C and cumulative total ecosystem C emissions. Yield curves strongly affected the predicted size of all five pools. Longer rotation lengths led to larger pools with the relative differences between rotation lengths varying with stand types. Pools associated with poplar were usually the largest while those of jack pine on shallow sites were generally the smallest. When compared to the starting point of the simulations, cumulative total ecosystem C and C emissions increased with the 100- and 250-year harvest rotations (HARV100 and HARV250, respectively) and declined with the 50-year harvest rotation (HARV50). Fire disturbances resulted in stable pools of cumulative ecosystem C and declines in C emissions. CBM-CFS3 provided realistic pool values but the authors suggest further development of the model depiction of ecosystem processes, especially with respect to the treatment of respiration. In general, the authors recommend that forest management planners consider using an integrated approach that links multiple proven and accepted models under appropriate model linking software.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".