Risk assessment framework for cumulative effects (RAFCE)
Bibliographic record
Abstract
Introduction: Regional environmental risk assessment is a practical approach to understanding and proactively addressing the cumulative effects of resource development in areas of regional importance. However, regional assessment is methodologically complex, and frameworks to identify and prioritize regional risk issues to guide effective management decisions are lacking. This research develops a risk and impacts-based cumulative effects assessment framework for scoping regional cumulative effects issues to guide present and future project and regional assessment. We operationalized the framework dubbed Risk Assessment Framework for Cumulative Effects (RAFCE) to assess the risks and impacts of proposed mining development in the Ring of Fire region of Northern Ontario, Canada. Methods: Methodologically, we built on existing studies to understand the key valued ecosystem components (VECs) impacted by mining; organized an expert Bowtie Risk Assessment Tool workshop and interviews to identify regional risks and define the VECs impacted by mining; and developed an impact prioritization model that helped quantify and prioritize impacts of mining. Results and Discussion: RAFCE enabled us to: a) identify drivers and impacts of cumulative effects and potential preventive and mitigation measures for effective cumulative effects management and b) describe, quantify, and rank the major impact and components of regional interest. Using RAFCE, we can identify and prioritize impacts that are cross-cutting, multisector‐driven, synergistic, and relevant to a region, visualize and understand the risk management process, identify policy and management issues to prevent risks or mitigate impacts, and ultimately inform resource allocation for effective regional cumulative effects assessment outcomes. RAFCE is suitable for engaging diverse stakeholders in planning for regional cumulative effects assessment.
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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 | 0.028 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".