Old-growth definitions and management: A literature review
Bibliographic record
Abstract
Over the past two decades, scientific discoveries have altered how forest management is viewed, including the understanding of late-successional or old-growth forest communities. Some accept that old-growth forests should be managed, but the process of identification and management of these forests has proven to be very difficult. This review examines literature on old growth and old-growth management from a broad North American base with a focus on the special issues associated with high-frequency forest disturbance regimes. The purpose of this paper is to: examine the various old-growth definitions and management approaches; review the importance of old-growth management and conservation; and draw conclusions and make recommendations based on the information reviewed. Old-growth definitions were divided into three categories: conceptual functional, conceptual structural, and quantitative working. The relative merits and challenges of each category are discussed using examples from different forest types across North America, but the focus is on northern fire-dependent forest ecosystems. The authors recommend the establishment of landscape-level objectives for old-growth retention that include: approaching management from an ecological perspective; recognizing the importance of varied natural disturbance patterns; increasing funds for detailed inventories (especially in more contentious or ecologically sensitive areas); developing a regional old-growth attribute scoring theme or index; using a top-down approach to old-growth management; and developing a monitoring plan to determine the effectiveness of established objectives.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".