Aggregating microsegments into harvest blocks by using spatial optimization and proximity objectives
Bibliographic record
Abstract
This study analyzed the performance of distance-based objective variables as an alternative to adjacency-based variables in spatial optimization when the aim is to aggregate small forest segments into harvest blocks. Distance-based objective variables maximized harvested volume within a certain distance from a harvested segment. Segments that constituted a harvest block did not have to be adjacent. It was hypothesized that it is more profitable to aggregate harvest blocks by using distance-based objective variables instead of adjacency-based objectives. It was also assumed that distance-based objectives result in harvest areas that correspond better to the harvest blocks of forestry practice. Distance-based objectives were tested with four maximum distances of uncut forest between two segments of the same harvest block. The tested distances were 0, 25, 100, and 300 m. A zero distance means that only adjacent segments form harvest blocks. The results showed that distance-based cutting aggregations improved net present value, as compared with adjacency-based cutting aggregation. Distance-based objective variables also resulted in larger harvest block size than adjacency-based objectives, if the maximum allowed distance of uncut forest between two harvested segments of the same harvest block was 25 m or longer and the average removal of a harvest block was 300 m3 or more. Requirement for adjacency led to small and compact harvest blocks.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".