Working Group on Cumulative Effects Assessment Approaches in Management(WGCEAM; outputs from 2024 meeting)
Bibliographic record
Abstract
The Working Group on Cumulative Effects Assessments in Management (WGCEAM) was established to develop a common framework for cumulative assessments to be applied in the context of ecosystem-based management.The objective was to develop a cumulative effects assessment framework to inform ecosystem-based management initiatives and demonstrate its application through regional case studies. Within the context of environmental policies, blue growth, and regional conventions, the framework is intended to inform strategic aspects of planning and management processes such as marine spatial planning and integrated marine management. It starts with the need to identify priorities across of the causal relationships between activities, their pressures on ecosystem components within the boundaries of an assessment area. The priority causal relationships are then used to establish vulnerability profiles based on exposure and effect potential calculation. Subsequently, exposure and effect potential clusters are used to characterize the pressures that should be subjected to management. Ultimately, the pressures based on the vulnerability profile of ecosystem components can be used to inform regulatory advisory processes for the activities generating these pressures. The case studies demonstrate that the framework can be used as guidance in a variety of impacts risk assessments for species and habitats in the North Sea, the German North Sea, and the Celtic Sea. The Canadian case studies also show that the framework does work for regulatory advisory processes to assess environmental impacts and the reliability and the effectiveness of technical measures. Guided by this framework, future work should be addressed through specific expert groups given the different management context and objectives that require.
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 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 | 0.103 | 0.071 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.014 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".