Harvesting fragmented boreal forest: system selection using a simulation-optimization approach
Bibliographic record
Abstract
Nordic forests, such as those found in Canada were known to offer opportunities for large and relatively homogeneous harvesting blocks. Increased fragmentation of forests makes operation planning more difficult and affects costs of road building and machinery relocation. Currently, the diversity of systems used for forest operations in eastern Canada represents only a fraction of existing alternatives. It might be that at least one alternative outperforms the systems currently used in the fragmented boreal forest. Hence, a subset of potential systems was identified in a preliminary evaluation of harvest systems in fragmented operations. From this sample, the objective is to develop a mathematical model to identify the harvest system with the lowest wood procurement cost in fragmented boreal forests. The candidate systems were simulated and optimized using a static deterministic approach. According to our results, the best systems in fragmented forests involve fewer machines, hence a lower relocation cost. Two CTL systems outperformed all others. The system using removable crane self-loading trucks for transport and the one using the forwarder for loading both resulted in 4 $/m3 (USD) advantage over the third least expensive system in the most fragmented harvest sites. The proposed model can be directly applied to assess harvest systems in other parts of the world with similar forest fragmentation challenges.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".