Future carbon storage in harvested wood products from Ontario’s Crown forests
Bibliographic record
Abstract
This analysis quantifies projected carbon (C) storage in harvested wood products (HWP) from Ontario’s Crown forests. The large-scale forest C budget model, FORCARB-ON, was applied to estimate HWP C stock changes using the production approach defined by the Intergovernmental Panel on Climate Change. Harvested wood volume was converted to C mass and allocated to four HWP end-use categories: in use, landfill, energy, and emission. The redistribution of C over time among HWP end-use categories was calculated using a product age-based C-distribution matrix. Carbon emissions for harvest, transport, and manufacturing, as well as emission reductions from the use of wood in place of other construction materials and fossil fuels were not accounted for. Considering the wood harvested from Ontario Crown forests from 1951 to 2000 and the projected harvest from 2001 to 2100, C storage in HWP in use and in landfills is projected to increase by 3.6 Mt·year–1 during 2001–2100, with an additional 1.2 Mt·year–1 burned for energy. Annual additions of C projected for HWP far outweighs the annual increase of C storage in Ontario’s Crown forests managed for harvest, which is projected to increase by 0.1 Mt·year–1 during the same period. These projections indicate that regulated harvest in Ontario results in a steadily increasing C sink in HWP and forests. Uncertainties in HWP C estimation are also discussed.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".