Hybrid poplar yields in Québec: Implications for a sustainable forest zoning management system
Bibliographic record
Abstract
In the province of Québec, approximately 12 000 ha of fast-growing poplar plantations are managed by industrials, while small private landowners have planted only 1000 ha. Most of these poplar plantations are established on clearcut forest sites (approx. 11 000 ha). What are the yields of these hybrid poplar plantations? In this article, available yield data are presented and discussed in the context of a sustainable forest zoning management system. In southern Québec, three factors are highly correlated to yield for clones of various parentages: NO3supply rate in riparian soils, elevation (or climate) and soil P availability in abandoned farmland soils. Many Québec forest sites, particularly in the boreal shield ecozone, have acidic soils and harsh climate, with low mineralization rates. They generally cannot fulfill the very high nutrient requirements of hybrid poplars. Within a forest zoning management system, hybrid poplar plantations and agroforestry should be located in priority in southern Québec landscapes, with low remaining natural forest cover, and where intensive agriculture is the dominant land-use. This strategy will increase biodiversity and the provision of ecosystem services. Elsewhere, intensive trembling aspen regeneration silviculture could be a sustainable alternative to forest conversion into hybrid poplar plantations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".