Analyse financière de scénarios sylvicoles visant la production de bois d’oeuvre de bouleaux jaune et à papier
Bibliographic record
Abstract
A financial profitability analysis is presented for a particular case study in Québec, using forest data coming from a management stratum located in the region of La Tuque, Quebec. Two silvicultural systems adapted to birch (Betula alleghaniensis Britton and Betula papyrifera Marsh.) regeneration and production, one based on shelterwood cutting and the other on patch cutting combined with single tree selection cutting, are compared over a 120-year period. The Sylva II model has been used to simulate stratum and wood products evolution through time. The financial performance of each scenario is described as the internal rate of return and the net present value. The results demonstrate that both treatments can be profitable and that patch clearcutting combined with the single tree selection cutting system is slightly more profitable than the second system evaluated. The sensitivity analysis shows that, from all criteria considered, treatment costs and product value are the most sensitive parameters. The evolution scenario parameter appears to be the less sensitive one. Finally, the product allocation matrix is the most sensitive of all silvicultural parameters. Although results are relevant for this particular case only, the approach can be broadly applied.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".