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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".