A modelling approach to inform regional cumulative effects assessment in northern Ontario
Bibliographic record
Abstract
Cumulatively, the effects of resource extraction and climate change have the potential to cause an unprecedented change to the ecosystems and livelihoods of Indigenous communities in Canada’s northern regions. Maintaining environmental and community values in the presence of such change will require a comprehensive understanding of potential long-term risks and opportunities to prevent or mitigate risks at the regional level. However, assessing the cumulative impact and benefits of the multiple disturbances at the regional level is complex due to the interaction of numerous drivers, values, actors, assessment scales, planning, and decision-making processes. We develop an integrated risk and cumulative effects (CE) management methodology to inform regional assessment by combining conceptual modelling through bowtie analysis and quantitative scenario analysis using ALCES Online (AO) simulation. We applied the framework using a case example in the Ring of Fire region of northern Ontario, Canada, to assess the CE of climate change, land use change, and wildlife harvest on the moose ( Alces alces )-wolf ( Canis lupus )-caribou ( Rangifer tarandus ) prey-predator system. The bowtie risk analysis provided a mechanism to define the management problem by identifying threats that contribute to risk, associated consequences, and specific management strategies that could be pursued under legislative frameworks and changes to maintain the sustainable dynamics of the moose-wolf-caribou system (MWC). The AO simulation of different levels of development and moose harvest, as well a baseline scenario that excluded climate change and development, allowed for a comprehensive examination of the complex processes driving the social-ecological system. The scenario analysis suggests that moose harvest can promote balance in the MWC system but only if applied in a sophisticated manner that limits moose harvest in areas with high road density, increases moose harvest in more remote regions susceptible to moose population expansion with climate change, and prioritizes Indigenous moose harvest to ensure a sustainable supply of moose for subsistence harvest. The case example shows how conceptual and quantitative modelling can provide the strategic perspective required for regional assessment.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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".