Environmental Management 001 \\ 0. \\ 007 /s00267-008-9\\93-4 Human Dimensions of Forest Disturbance by Insects: An International Synthesis
Bibliographic record
Abstract
Abstract Ecological disturbances of forests by insects have a complex array of associated human dimensions presenting complications for natural resource decision making and relationships between stakeholders and man-agers. This article discusses the human context of forest disturbances by insects by reviewing four cases of bark beetle forest disturbance from British Columbia in Canada, Bavarian Forest National Park in Germany, the Kenai Peninsula in Alaska, and the north central region of Col-orado. Findings and lessons learned from these studies are outlined along with their implications for managing forest disturbances by insects in general. Conclusions focus on the need to assess the broad array of impacts and risks perceived by local residents and the capacity for local action and involvement in managing forest disturbances. Communication and interaction between resource manag-ers and local stakeholders can facilitate the identification of management priorities and potentially reduce some of the risks associated with forest disturbances by insects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.134 | 0.006 |
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".