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 m 3 or more. Requirement for adjacency led to small and compact harvest blocks.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 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".