Ontario’s managed forests and harvested wood products contribute to greenhouse gas mitigation from 2020 to 2100
Bibliographic record
Abstract
We used an integrated approach to estimate the greenhouse gas (GHG) mitigation potential of Ontario’s forestry sector, defined as the managed forests and the harvested wood products (HWP) originating from these forests. The 44.7 million ha of managed forests in this study included Crown forests designated as 41 forest management units (FMUs) for timber harvesting, productive forests north of the area of undertaking, large parks, and private forest land. Forests and HWP were simulated from approximately 2010 to 2100, with carbon (C) stocks and emissions reported for the period 2020 to 2100. A baseline scenario was defined to represent business as usual forestry operations in Ontario, in which the 41 FMUs and the private forests were harvested at historical (1990–2009) rates, and HWP production and end uses were assumed to follow Ontario’s historical values (1991–2010). In the baseline scenario, the forest C stocks were projected to increase from 7229.7 million tonnes (Mt C) in 2020 to 7424 Mt C in 2100. Th...
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".