Optimal Forest Stand Aggregation and Harvest Scheduling Using Compactly Formulated Integer Programming
Bibliographic record
Abstract
We propose an alternative approach for optimal forest stand aggregation for implementing harvest scheduling, which allows for multiple harvests using a compact formulated integer programming that seeks an optimal aggregated pattern among candidates for forest management units over the planning horizon. We deal with aggregation of small forest stands by introducing the concept of a "hyper unit" as a possible aggregated management unit, which is predefined with the use of adjacency relationship among the set of forest stands. Our proposed approach is based on an optimization framework of a traditional spatially constrained harvest scheduling problem which is used to choose the best set of treatments for the aggregated management units, as well as the original un-aggregated forest stands, while allowing for multiple harvests. We also apply adjacency constraints to create aggregated management units, which are separated from other units, as well as un-aggregated forest stands such that, no adjacent units are harvested in the same period.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".