Scheduling forest core area production using mixed integer programming
Bibliographic record
Abstract
Core area, the area of mature forest protected by a buffer area from edge effects of surrounding habitats, is an important spatial measure describing forest ecological conditions. Three alternative mixed integer programming (MIP) formulations are presented for explicitly scheduling core area production in a forest management scheduling model. Formulations utilize detailed data preprocessing that develops a set of influence zones. Each influence zone identifies an area of the forest that can produce core area. Each zone is influenced by a unique combination of management units (stands) of the forest. The assumed width of the buffer surrounding core area affects both the number of zones in the forest and the number of stands associated with each zone. Numerous test cases were applied, varying the MIP formulation used to describe core area production, the assumed buffer for core area (50 or 100 m), and the set of additional forest-wide constraints to control harvest levels and core area production levels over time. Solution times varied substantially between the alternative MIP formulations. Solution times were substantially less for the formulation that used more, but simpler, spatial constraints. Solution times for large test cases suggest that real-world applications are likely feasible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".