Developing landscape connectivity in commercial boreal forests using minimum spanning tree and spatial optimization
Bibliographic record
Abstract
Currently, habitat connectivity is poorly integrated in forest-planning calculations related to decision-making in commercial boreal forests. This study developed a method that utilizes graph theory and minimum spanning tree (MST) to improve the connectivity of broadleaf-rich habitats in such forests. The location of created habitat corridors could change over time, and the method did not require adjacency between the stands that constituted the MST. Losses in net present value (NPV) due to improved connectivity were also examined. The planning area was located in southern Finland and included 1040 forest stands. Treatment schedules for the stands were created using simulation software, and heuristic optimization methods were used to find optimal treatments for the stands to meet the specified objectives. Incorporating even-flow harvest removals and NPV in an objective function provided real-world conditions in the optimization framework. The developed method clearly improved the connectivity of broadleaf-rich patches. The monetary losses of improved connectivity were moderate compared with the ecological-based connectivity benefits gained with the method. The developed MST method can be applied to any desired forest feature and modified to work in various situations related to connectivity problems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".