Developing a production possibility set of wildlife species persistence and timber harvest value
Bibliographic record
Abstract
An integrated model, combining spatial wildlife population and timber harvest and growth models, was developed to explore tradeoffs between the likelihood of persistence of a wildlife species, the northern flying squirrel (Glaucomys sabrinus), and timber production on a landscape on the west side of the Oregon Cascade Range. A simplified wildlife model was developed from the fully parameterized spatial wildlife model, using a habitat neighborhood-weighting scheme, for use in the optimization. Simulated annealing, a heuristic optimization technique, was used to solve for harvest schedules that maximized the net present value of timber harvest subject to a target value for likelihood of species persistence over a 100-year planning period. By solving this problem for a range of species persistence targets, a production possibility frontier was developed that showed tradeoffs between timber harvest value and likelihood of species persistence on this landscape. Although the results are specific to the wildlife species and the landscape analyzed, the approach is general and provides a structure for future models that will help land managers and forest planners to understand tradeoffs among competing resource uses.
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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 | 0.003 |
| 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 | 0.002 | 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".