Scientific considerations and challenges for addressing cumulative effects in forest landscapes in Canada
Bibliographic record
Abstract
Traditionally, forest management has focused on forestry-related practices whereas other industries have been managed separately. Forest management requires the integration of all natural resource development activities, along with other anthropogenic and natural forest disturbances (e.g., climate change, pollution, wildfire, pest disturbance) to understand how human activities can change forested ecosystems. The term cumulative effects has been used to describe these attempts to integrate all disturbances to develop an understanding of past, current, and future impacts on environmental, social, and economic components of the system. In this review, we focus on the science required to understand the past, current, and future impacts of the cumulative effects of anthropogenic and natural disturbances on forested ecosystems or their components. We have primarily focused on the terrestrial system with an emphasis on northern forests in Canada. Our paper is not intended to be a comprehensive review of all cumulative effects science but a synthesis of the challenges and approaches currently being used. Central repositories were identified as an approach to deal with issues of availability of remotely sensed data on anthropogenic and natural disturbances. Data integration projects, open data, and well-designed large-scale data collection efforts are needed to provide sufficient data on environmental responses to cumulative effects. As well, large-scale integrated, modularized ecosystem models are needed to bring stressor and environmental response data together to explore responses to, and interactions between, multiple stressors to project these effects into the future and to identify future data collection needs.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".