Creating land allocation zones for forest management: a simulated annealing approach
Bibliographic record
Abstract
This paper describes the Zone Allocation Model (ZAM) that uses the simulated annealing algorithm to create forest management zones. ZAM partitions the landscape into the Timber, Habitat, and Old Growth zones by allocating small land tiles into contiguous areas. The zone allocation process is guided by landscape-level targets and size and shape objectives. An ecological representation objective proportionally distributes all ecosystem types into each of the three zones. Priority objectives control allocation of identified lands that are targeted for specific zones. All objectives are combined within an objective function, with a penalty-weighting system specifying relative importance of each objective. The ZAM model found 1.7%4.4% of theoretical optimum scores from small to large problems, respectively. A demonstration on a 1.2 × 10 6 ha landscape from coastal British Columbia illustrates the iterative exploration of compromises between objectives that leads to informed zone allocation decisions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".