Adaptive Ecosystem Management in the Pacific Northwest: a Case Study from Coastal Oregon
Bibliographic record
Abstract
Adaptive ecosystem management has been adopted as a goal for decision making by several of the land management and regulatory agencies of the U.S. government. One of the first attempts to implement ecosystem management was undertaken on the federally managed forests of the Pacific Northwest in 1994. In addition to a network of reserve areas intended to restore habitat for late-successional terrestrial and aquatic species, "adaptive management areas" (AMAs) were established. These AMAs were intended to be focal areas for implementing innovative methods of ecological conservation and restoration and meeting economic and social goals. This paper analyzes the primary ecological, social, and institutional issues of concern to one AMA in the Coast Range in northern Oregon. Based on existing knowledge, several divergent approaches are available that could meet ecological goals, but these approaches differ greatly in their social and economic implications. In particular, approaches that rely on the natural succession of the existing landscape or attempt to recreate historical patterns may not meet ecosystem goals for restoration as readily as an approach based on the active manipulation of existing structure and composition. In addition, institutions are still adjusting to recent changes in management priorities. Although some innovative projects have been developed, adaptive management in its most rigorous sense is still in its infancy. Indeed, functional social networks that support adaptive management may be required before policy and scientific innovations can be realized. The obstacles to adaptive management in this case are similar to those encountered by other efforts of this type, but the solutions will probably have to be local and idiosyncratic to be effective.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".