Bibliographic record
Abstract
Canada is seeking cost-effective means to mitigate greenhouse gas emissions, particularly CO2. One of the promising means is the short rotation woody crops (SRWC) plantation, a silvicultural approach to establishing and managing fast growing plantations on previously cleared lands. This paper utilizes the data set provided by recent harvesting operations at the Ellerslie SRWC Technical Development Site in Edmonton (Alberta) to assess the ability of SRWC using High Yield Afforestation to mitigate GHG emissions and generate more wood fibre and the investment attractiveness to establish future plantations. Results illustrate that at current trading prices for carbon credits and market prices of woodchips, expected rates of return on investment for SRWC were relatively low, despite a positive net present value ($400/ha for 20 year planting cycle without carbon credits). However, estimates from the Ellerslie site indicate that 330 tons of CO2-eq per hectare are captured above and below ground over the 20 year plantation cycle. However, higher future carbon prices, a well-developed market for buying and selling carbon credits, as well as adapted policy including additional government sponsored programs for carbon credits, could make SRWC more attractive and dramatically change the economics of afforestation in the future.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".