Optimal Corridor Connection Considering Forage Reserve within Spatially Constrained Harvest Scheduling under Area Restrictions
Bibliographic record
Abstract
Integer programming has been extensively utilized for solving forest management planning or spatially constrained harvest scheduling problems in the past decades. In addition to determining the timing and location of harvest activities over the forest landscape, there are other environmental requirements that call for the setting aside of forest units for conservation purposes. The creation of contiguous forest stands for the protection of wildlife habitat protection can be one of those requirements. A review of existing literature on environmental management shows that a great deal of attention has been paid to nature reserve design in the selection of corridor connection among fragmented habitats. In this paper, we present a new exact optimization model which uses mixed integer programming framework to seek optimal corridor connection and the selection of suitable forage reserves from fragmented habitats, in a spatially constrained harvest scheduling problem under maximum opening size requirements, over space and time. We rely on the concept of the maximum flow problem to deal with spatial aggregation for forest units as well as corridor connection and forage reserve network. The proposed model does not need a priori enumeration and allows for multiple harvests over time. In addition to corridor connection, our novel approach takes into account forage reserves within an exact solution framework of an area restriction model.
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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".