A retrospective and lessons learned from Natural Resources Canada’s Forest 2020 afforestation initiative
Bibliographic record
Abstract
Canada is seeking cost-effective means to mitigate anthropogenic greenhouse gas emissions, particularly CO2, that have been linked to global climate change. In 2003 the Government of Canada launched the Forest 2020 Plantation Development and Assessment Initiative to assess the potential for fast-growing woody crops to sequester carbon from the atmosphere. Across the country 6000 ha of plantations were established and monitored on nonforested lands (afforestation) using a variety of methods. Economic analyses assessed the investment attractiveness of this mitigation measure for a range of species and suitable lands, taking into account such factors as growth rates, agricultural opportunity costs and a range of possible carbon values. Analyses illustrated that at current trading prices for carbon and for much of the available lands and expanding markets for forest bioproducts, expected rates of return on investment for afforestation were relatively low. However, higher future carbon prices, combined with monetary values for environmental benefits, could dramatically change the economics of afforestation in the future. Key words: afforestation, carbon sequestration, forest carbon offset project, climate change mitigation, policy analysis, risk analysis, forest investment analysis, hybrids, hybrid poplar, fast-growing trees
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".