Bibliographic record
Abstract
This book arose from an attempt to solve a complex, real-world problemhow do we sustain biological diversity in managed forests?MacMillan Bloedel harvested natural forests in coastal temperate rainforest, largely on public land.It had the largest forest holdings in coastal British Columbia and faced effective market campaigns to stop clear-cutting of old growth and reduce its harvest.In 1998, the company adopted a completely new form of forestry that it believed could be shown to sustain native species richness in coastal temperate rainforests.Shortly after making this commitment, MacMillan Bloedel was acquired by Weyerhaeuser Company.Weyerhaeuser maintained the commitment to evaluating ecological consequences of forest practices, so efforts to learn how to sustain biodiversity in managed forests also continued.The forest planning and practices introduced (zoning and variable retention) were novel, as was the large-scale effort to evaluate the effectiveness of these practices within an adaptive management program.As part of its quality control in developing and implementing its adaptive management program, Weyerhaeuser hosted meetings of an International Scientific Advisory Panel.Panel members were recognized experts drawn from Australia, Europe, Canada, and the United States.These members praised the efforts and accomplishments of the adaptive management program and were instrumental in Weyerhaeuser receiving the Ecological Society of America's Corporate Award in 2001.The panel also urged the authors to publish the approach, yielding this book.The book is divided into three parts.Part 1, "Introduction," introduces the generic nature of the problem, including elements of wicked problems and complex challenges faced by forest managers, plus a potential solution to the problem, an effective adaptive management program.Part 2, "The Indicators," treats the major indicators of success in sustaining biological diversity and learning acquired from evaluating these indicators.Part 3,
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.444 | 0.293 |
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".