Bibliographic record
Abstract
How much old growth is there? How much was there? Is remaining old growth disappearing? If so, how fast and why? Is any more old growth being created? How fragmented are old-growth forests? What other forest types and land uses surround them? Scientists see these as challenging questions, never to be fully resolved. Policy makers see information gaps and want answers. They assume that because the public values old-growth forests, their continuing availability must be assured. Forest managers need to be convinced that old-growth forests provide unique values before taking costly measures to conserve them. The relative stability of old-growth forests is interesting from a management perspective. Are old-growth forests more resistant to high-intensity disturbances, such as crown fires and violent storms? Do they resist insect outbreaks? A related issue is the quality of ecosystem services provided by old-growth forests. Do they have an exceptional ability to provide clean water, to stabilize hydrologic regimes, and to moderate local climates? Can they be used to test hypotheses about complexity, stability, resilience, and ecosystem change? These questions provide a strong rationale for developing working definitions of old-growth forests, for retaining areas of old-growth forest, and for replicating old-growth features in landscapes managed for timber production. Old-growth forests are desirable sites for monitoring, serving as benchmarks for adaptive management. Knowledge about old-growth forests has already had a considerable impact on policy and management, particularly in coastal regions. Current research and monitoring systems may not be adequate for the task of identifying and describing the biological complexity and diversity inherent in old-growth forests. New investments in collecting and managing data from old-growth (and secondary) forests are needed, and will pay manifold dividends to future generations of Canadians. This paper suggests that the central role of old-growth forests in developing sustainable forest management should create an incentive for the forest science, policy, and management communities to unite in support of their conservation. Key words: biodiversity, gene conservation, resilience, ecosystem approach, information management, ecosystem services
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 | 0.114 | 0.316 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.027 |
| Open science | 0.014 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".