Natural disturbance emulation in boreal forest ecosystem management — theories, strategies, and a comparison with conventional even-aged management<sup>1</sup>This article is one of a selection of papers from the 7th International Conference on Disturbance Dynamics in Boreal Forests.
Bibliographic record
Abstract
Natural disturbance emulation (NDE) has been proposed as a general approach to ecologically sustainable forest management. We reviewed the concepts, theories, and strategies related to NDE in boreal forest management. We also reviewed publications that discussed NDE in the boreal forest in general and those that specifically compared NDE-based management with conventional even-aged management. The papers generally focused on northern North America and landscape-scale wildfire as the main disturbance factor, whereas information from Eurasia was exclusively theoretical. Within this limited scope, NDE was generally found to have a positive effect on biodiversity in terms of forest structure and species diversity when compared with conventional even-aged management. Studies on timber supply and social implications of NDE were so few that they preclude generalizations. We conclude that the ecological and economic performance of NDE as a management approach still remains poorly examined. To advance the development of NDE, particular attention should be given to (1) augmenting the knowledge base on natural range of variability of unmanaged forest ecosystems and evaluating the validity of this information in a changing climate, (2) fostering multidisciplinary research with better integration of ecological theory to both integrative and analytical research on NDE, and (3) better integration of socioeconomic concerns, adaptive management schemes, and international collaboration into NDE initiatives.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".