Making adaptive management for biodiversity work the example of Weyerhaeuser in coastal British Columbia
Bibliographic record
Abstract
In 1998, MacMillan Bloedel (now Weyerhaeuser) committed to a system of Stewardship Zones and to replacing clearcutting with variable retention over its 1.1 million ha coastal tenure. The decision began a grand experiment in forest planning and practice, which the company committed to monitor and refine through an adaptive management program. The program was most challenging to design and implement for biodiversity. Key elements of the program were: creating a criterion and associated indicators, developing a list of focused questions, and developing a cost-effective design for monitoring and learning. The final step in any adaptive management program is linking the monitoring back to specific management actions. We provide examples of successful linkages back for each of the three major indicators of biodiversity: ecosystem representation, habitat structure, and organisms. We discuss major difficulties that arise when developing management responses to the complex issue of sustaining biological diversity and note four major challenges to the design and implementation of any adaptive management program. Key words: adaptive management, biodiversity, indicators, monitoring, variable retention harvesting
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.013 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".