Integration of bioenergy strategies into forest management scenarios for Crown land in New Brunswick, Canada
Bibliographic record
Abstract
About 70% (110 PJ) of energy used in New Brunswick is sourced from fossil fuels, and its high cost and uncertain long-term supply have renewed interest in bioenergy production. To evaluate opportunities for sourcing bioenergy from the forest, we integrated bioenergy and timber production into a forest estate model and evaluated joint production scenarios for 3.3 million hectares of Crown land in New Brunswick over a 100-year horizon. Scenarios included maximizing timber or bioenergy production under three timing preferences (expressed as discount rates) and various combinations of harvest residues, pulpwood biomass, and willow ( Salix spp.) plantations. Under scenarios that allocated 66% of harvest residues and 30% of pulpwood to bioenergy production, maximizing discounted (8%) timber or bioenergy, respectively, generated average timber harvests of 6.51 and 6.26 Mm3·year–1 and bioenergy outputs equivalent to 30% and 32% of provincial fossil fuel consumption. Introducing 40 000 ha of willow plantations under the maximize bioenergy scenario yielded bioenergy equivalent to 41% of provincial fossil fuel consumption while maintaining the timber harvest at 6.21 Mm3·year–1. Our study demonstrates a framework for integrating bioenergy and timber production in forest management design and quantifies the significant potential for obtaining both bioenergy and timber from the forest.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".