Bibliographic record
Abstract
Abstract Forests long have been important to humanity and other species of the planet, providing timber and non-timber resources, innumerable ecosystem services, and supporting biological diversity. The technical determination of requirements for a sustained yield of timber was a revolutionary achievement, which has since been extended to the sustainability of other aspects of forests and diverse human endeavors. Yet the expectations of stasis and constancy make sustainability difficult in a world undergoing rapid changes, necessitating a paradigm shift that accommodates uncertainty and embraces change. Resilience theory and the principles of complex adaptive systems provide a foundation for a resilient and adaptive approach to forest stewardship. The many services, uses, values, and expectations of forest often conflict with each other, requiring bundling into protection, multi-purpose, and timber zones. Applying resistance, recovery, adjustment, reconfiguration, or transformation strategies for resilient forest management is a place-based exercise, addressing local attributes, vulnerabilities, and priorities. Climate change and its stimulation of forest disturbances are priority challenges for which resilience planning and management is needed. The diversity, disturbance regimes, biological legacies, and spatial patterns of natural forests inspire an ecological forestry approach that has the greatest potential for supporting forest resilience, and ultimately the resilience of forest enterprises and forest communities. A long history of deforestation and forest degradation often requires forest restoration to make forests more resilient. With many unknowns and much uncertainty, resilient forest management takes place in an adaptive management context. Resilient forest stewardship is ultimately about people management and must take place within a supportive governance and sociocultural framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".