A flexible framework for species-based regional cumulative effects assessments to support offshore wind energy planning and management
Bibliographic record
Abstract
Offshore wind energy development (OWED) is pivotal for renewable energy transition and climate resiliency. However, OWED activities may negatively affect wildlife, contributing to cumulative effects (CE) from human activities and natural processes. Cumulative effects assessments (CEAs) are vital for informed planning and management of OWED activities during regional assessment, site selection, and site evaluation phases. To reduce impacts on wildlife, OWEDs should be sited in areas that avoid or minimize CE. We present a flexible, species-based framework to assess CE from OWED activities and other pressures, supporting decision-making in early planning phases. The framework uses a species-based approach, applicable to various wildlife receptors (i.e., species or populations), and adapts to available information on ecology, socioeconomics, and pressures. The analytical strategy uses a CE metric to indicate the presence or magnitude of effects from all pressures on receptors. Spatially explicit optimization methods identify OWED site configurations that minimize a CE metric. The framework accommodates alternative pressure scenarios that include foreseeable future human activities and natural processes and can explore the sensitivity of the results to uncertain parameters. Given sufficient spatial information on receptor density, pressure magnitude, and cause-effect pathways, the spatial optimization algorithm can find solutions that minimize species- or population-level impacts from CE. If this ideal standard cannot be achieved due to information gaps, alternative metrics may be used to inform the immediate decision-making process. This framework offers a practical approach for balancing renewable energy goals with wildlife conservation, even when information is incomplete. • Step-by-step framework for cumulative effects assessment on any wildlife species or population. • Regional approach adaptable to diverse species and pressure data types. • Spatial optimization identifies offshore wind site configurations minimizing impacts. • Accommodates alternative scenarios, exploring sensitivity to uncertain parameters. • Standardized metrics enable consistent and comparable cumulative effects assessments.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".