Research priorities for sound and vibration effects on fishes and aquatic invertebrates from offshore wind energy development
Bibliographic record
Abstract
There are substantial knowledge gaps regarding both the bioacoustics and the responses of animals to sounds associated with pre-construction, construction, and operations of offshore wind (OSW) energy development. A workgroup of the 2020 State of the Science Workshop on Wildlife and Offshore Wind Energy recommended priority studies for the next five years to help stakeholders better understand potential cumulative biological impacts of sound and vibration to fishes and aquatic invertebrates as the OSW industry develops. The workgroup identified seven short-term priorities that include a mix of primary research and coordination efforts. Key research needs include the examination of animal displacement and other behavioral responses to sound, as well as hearing sensitivity studies related to particle motion, substrate vibration, and sound pressure. Other needs include: identification of priority taxa on which to focus research; standardization of methods; development of a long-term highly instrumented field site; and examination of sound mitigation options for fishes and aquatic invertebrates. Effective assessment of potential cumulative impacts of sound and vibration on fishes and aquatic invertebrates is currently precluded by these and other knowledge gaps. Filling critical gaps in knowledge will improve our understanding of possible sound-related impacts of OSW energy development to populations and ecosystems.
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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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".