Dynamic treatment units in forest planning using cell proximity
Bibliographic record
Abstract
In forest management planning, the dynamic treatment unit (DTU) approach has become an increasingly relevant alternative to the traditional planning approach using fixed stands, due to improved remote sensing techniques and optimization procedures, with the potential for the higher goal fulfillment of forest activities. For the DTU approach, the traditional concept of fixed stands is disregarded, and forest data are kept in units with a high spatial resolution. Forest operations are planned by clustering cells to form treatment units for harvest operations. This paper presents a new model with an exact optimization technique for forming DTUs in forest planning. In comparison with most previous models, this model aims for increased flexibility by modelling the spatial dimension according to cell proximity rather than immediate adjacency. The model is evaluated using a case study with harvest flow constraints for a forest estate in southern Sweden, represented by 3587 cells. The parameter settings differed between cases, resulting in varying degrees of clustered DTUs, which caused relative net present value losses of up to 4.3%. The case without clustering had the lowest net present value when considering entry costs. The solution times varied between 2.2 s and 42 min 6 s and grew rapidly with increasing problem size.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".