Beautiful Plantations: can intensive silviculture help Canada to fulfill ecological and timber production objectives?
Bibliographic record
Abstract
There is growing international agreement that intensive silviculture will play a major role in meeting future demand for wood and wood fibre worldwide. In Canada, however, extensive forest management continues to be the dominant paradigm. Driven by low growth rates in primary forests and the consequent long rotations, current policies support only basic management, with little or no silvicultural intervention between stand initiation and final harvests. By contrast, native conifers and hybrid poplars (Populus spp.) grown in plantations have been shown to achieve increments of 6 to 29 m 3 ha -1 yr -1 in Canada. In this paper, we argue that increased production, economic, and environmental benefits can be realized in Canada by intensifying silvicultural practices over designated parts of the landbase. Indeed, the shift to intensive management may be essential to sustain Canada’s competitiveness in the international forest products sector. In reviewing past work, we demonstrate that intensive silviculture may yield outputs that are competitive with many other regions, even those in the tropics. Achieving wide support for intensive silviculture will require integration of a broader range of silvicultural, environmental, and social objectives into management planning than has traditionally been the case. Such a broad-based strategy, especially where it has gained the support of communities, may be the most balanced and effective means of resolving many of the key forest management issues that face Canada in the 21 st Century. Key words: conventional intensive silviculture, super-intensive silviculture, plantations, foreign competition, multiple-use forests, native conifers, roads, CO 2 emissions, incentive
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".