A Multi-Criterion Evaluation Process for Determining Cost-Effective Harvesting Systems in Fragmented Boreal Forests
Bibliographic record
Abstract
Nordic forests, like those found in Canada, used to consist of large and relatively homogeneous mature stands. Such a spatial pattern allows for harvest operations to be highly concentrated, minimizing procurement costs. However, the growing fragmentation of these forests makes planning difficult and increases the costs of road building and machinery relocation. While operational solutions have been developed in regions with small harvest areas, their transferability to different settings is unknown. Finding the most suitable combination of equipment for a given context is challenging considering the multitude of possibilities. The objective of this study is to identify, from all possible options, a subset of harvest systems expected to perform well in fragmented boreal forests. The results from this research are two-fold. First, a comprehensive review of forest machines and harvest systems is provided. Second, a multi-criteria decision analysis (MCDA) methodology is proposed to evaluate the alternatives. In a boreal forest context, the conventional harvester-forwarder system (CTL) was ranked among the best solutions, along with mild adaptations of the usual configurations. Several whole-tree (WT) system configurations were also highly ranked. While the results are specific to the case studied, the review and selection methodology can serve in different operational contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".