An inventory-based analysis of Canada's managed forest carbon dynamics, 1990 to 2008
Bibliographic record
Abstract
Canada's forests play an important role in the global carbon (C) cycle because of their large and dynamic C stocks. Detailed monitoring of C exchange between forests and the atmosphere and improved understanding of the processes that affect the net ecosystem exchange of C are needed to improve our understanding of the terrestrial C budget. We estimated the C budget of Canada's 2.3 × 106 km2 managed forests from 1990 to 2008 using an empirical modelling approach driven by detailed forestry datasets. We estimated that average net primary production (NPP) during this period was 809 ± 5 Tg C yr−1 (352 g C m−2 yr−1) and net ecosystem production (NEP) was 71 ± 9 Tg C yr−1 (31 g C m−2 yr−1). Harvesting transferred 45 ± 4 Tg C yr−1 out of the ecosystem and 45 ± 4 Tg C yr−1 within the ecosystem (from living biomass to dead organic matter pools). Fires released 23 ± 16 Tg C yr−1 directly to the atmosphere, and fires, insects and other natural disturbances transferred 52 ± 41 Tg C yr−1 from biomass to dead organic matter pools, from where C will gradually be released through decomposition. Net biome production (NBP) was only 2 ± 20 Tg C yr−1 (1 g C m−2 yr−1); the low C sequestration ratio (NBP/NPP=0.3%) is attributed to the high average age of Canada's managed forests and the impact of natural disturbances. Although net losses of ecosystem C occurred during several years due to large fires and widespread bark beetle outbreak, Canada's managed forests were a sink for atmospheric CO2 in all years, with an uptake of 50 ± 18 Tg C yr−1 [net ecosystem exchange (NEE) of CO2=−22 g C m−2 yr−1].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| 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) | 0.001 | 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".