The Opportunities and Barriers in Considering Cumulative Effects for Landscape Assessments
Bibliographic record
Abstract
Cumulative effects are defined as the joint and aggregated effects of many factors and processes. Their consideration in landscape and environmental assessments are integral at both the project and strategic level, and they can help to bridge the different spatial and temporal scales. The primary challenge of conducting cumulative effect assessments (CEAs) is the difficulty in understanding the complicated nature of cumulative effects. We used three criteria for a systematic understanding of the barriers to addressing cumulative effects that are critical for improving knowledge systems for sustainable development and environmental assessment: salience, credibility, and legitimacy, and analyzed three cases through a variety of studies and resources: the Middle Humber in the U.K., the Transboundary Crown of the Continent in the U.S. and Canada, and the Great Sandhills in Canada, to understand how CEAs have been applied and obstructed in terms of the three criteria. In addition, a series of focus group interviews with experts and practitioners were performed to illuminate the critical barriers based on the criteria for addressing CEA in the context of South Korea. Based on the lessons, we suggest several key strategies such as securing a cooperative consulting process, and active and transparent partnerships; using a strategic environmental assessment as a framework; understanding and incorporating stakeholder knowledge; using advanced computer modeling and simulation techniques including effective visualization tools; and preparing a simple model design and understandable scientific information.
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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 | 0.165 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".