Evaluating the profitability of selection cuts in irregular boreal forests: an approach based on Monte Carlo simulations
Bibliographic record
Abstract
This study compares the financial return of converting old-growth boreal stands into even-aged stands to that of two approaches of selection cutting in Quebec (Canada). In this region, old-growth stands are usually harvested by completely removing the canopy while protecting the abundant advance regeneration, an approach known as careful logging around advance growth (CLAAG). These approaches were compared using a time frame of over 200 years. Consideration is given to the majority of the operating costs leading to end products. The financial analysis integrates Monte Carlo simulations, making it possible to consider the uncertainty associated with variables. The net present values (NPVs) are then associated with a distribution of probabilities. The results show that the probabilities of obtaining positive NPVs are high for all treatments, suggesting that selection cutting approaches can be appropriate alternatives to CLAAG for some stands. Depending on the criteria used, the CLAAG cut or one of the selection cuts show the best performances. In fact, the results of the financial study show that in the future, selection cutting approaches will be more profitable than CLAAG but still less than present CLAAG operations. This occurs because, according to the current yield curves and rotation ages, future stands managed with CLAAG will have smaller and less valuable trees than in the primary forest and in stands managed with the selection system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".