A systematic map of systematic reviews and meta-analyses on anthropogenic noise impact on wildlife
Bibliographic record
Abstract
As systematic reviews on the effects of anthropogenic noise on wildlife increasingly inform policy, a critical evaluation of this secondary evidence is essential. We assessed the coverage, methodological quality, and policy relevance of existing syntheses in this field. Following a preregistered protocol, we conducted a systematic search using Scopus, Web of Science, and Google Scholar, that identified 50 syntheses (systematic reviews, maps, and meta-analyses). Of these 50 syntheses, we included 47 in the bibliometric analysis, 23 in the policy attention analysis, and 44 in the critical appraisal. The included syntheses were published between 2008 and 2025, but mainly in the last six years, and focused on behavioural, physiological, and communication outcomes in animals. Most syntheses looked at the effects of transportation and energy industry activities. Syntheses were most likely to review evidence from marine, followed by terrestrial ecosystems. We found critical gaps in the coverage in terms of their taxonomic scope, with notable underrepresentation of invertebrates, amphibians, and reptiles. Most syntheses were first-authored by researchers based in the United Kingdom, Canada, and the United States. Although many syntheses had authors from more than one country, authors from non-English-speaking countries were largely absent. Almost half of syntheses were cited in policy documents, mainly government policies and regulatory submissions. Syntheses of evidence on marine environments received the most policy citations and urban noise the least. There was no significant difference in quality scores between policy-cited and non-cited syntheses, and most of them are rated low due to methodological and reporting shortcomings. Given these findings, it is critical to fill the synthesis gaps and improve methodology and reporting of future evidence syntheses in this area.
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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 | 0.070 | 0.288 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.177 | 0.130 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".