Revised historic harvest data improve estimates of the impacts of human activities on reported greenhouse gas emissions and removals in Canada’s managed forest
Bibliographic record
Abstract
Guidelines for international reporting of greenhouse gas emissions and removals in the forest sector require a land-based approach that includes all lands subject to forest management activities such as harvest, forest inventory, regeneration, management of natural disturbances, and protected areas. The reported net greenhouse gas balance of managed forests is not limited to forest stands resulting from timber harvest and wood product use. Reporting guidelines specify methods to reduce interannual variability in reported emissions attributable to natural disturbances. In Canada, the initial (1990) assignment of all inventoried stands to anthropogenic or natural disturbance reporting categories is determined by the last stand-initiating disturbance. A new compilation of historic (1889 to 1989) harvest data in Canada reduces the area reported in the anthropogenic category by 34 million hectares (20%) in 1990. This area transfer from anthropogenic to natural origin reduces the carbon sink reported as anthropogenic by 113 Mt CO2e yr−1 (56%) in 1990 and by 30 Mt CO2e yr−1 (23%) in 2021.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".