Minimizing spatial dispersion of forest harvest areas using spectral clustering and set covering modelling
Bibliographic record
Abstract
In recent years, inclusion of spatial considerations in forest operations management has attracted great attention of both researchers and practitioners. In the province of Quebec, Canada, forest product companies subcontract harvesting operations to contractors. One of the challenges faced by the harvesting teams relates to moving the harvesting machineries between harvest areas, which is usually very costly and time consuming. To facilitate these operations, we propose a planning support tool to group the harvest areas in a way that the spatial dispersion of the clusters is reduced. We employed the spectral clustering algorithm to generate many alternative clusters of a set of harvest areas based on their transportation distance from one another and their available timber volume. Afterwards, a set covering model is developed to choose the clusters corresponding to the least spatial dispersion and approximately equal volume of timber. The approach is tested in a real case in Quebec and the results of two versions of set covering model were compared and analyzed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".