Are operational plantations meeting expectations? A large-scale assessment of realized versus anticipated yield in eastern Canada
Bibliographic record
Abstract
Forest plantations play an increasingly important role in meeting global demand for wood. They usually have higher yield than naturally regenerated forests. Thus, plantations can support economically viable wood production, enable forest conservation elsewere, help mitigate climate change by contributing to carbon sequestration and increase forest resilience and resistance to biotic and abiotic stressors. If yield of plantations is not as high as anticipated, then their use could generate important sustainability issues. There are still major gaps in our understanding of the factors that influence yield, even with respect to black spruce, white spruce, and jack pine, three of the most commonly planted tree species in northeastern North America. Our objective was to evaluate the yield of forest plantations of these species over a 416 000 km2 region that was representative of northeastern North American forests. Contrary to our prediction, realized yield of operational plantations was consistently lower than anticipated. Site index and competition both played a significant role in determining the yield of plantations. In the context of uncertain realized yield of operational plantations, we emphasize the necessity of relying on adaptive management to determine harvest levels that are compatible with sustainable management objectives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".