Profiling research on <scp>PFAS</scp> in wildlife: Systematic evidence map and bibliometric analysis
Bibliographic record
Abstract
Abstract Per‐ and polyfluoroalkyl substances (PFAS) are a large group of synthetic chemicals that have been in use for over 70 years. Their ubiquitous distribution and harmful effects pose a threat to wildlife worldwide. To provide a comprehensive synopsis and show the gaps and gluts of existing research on PFAS exposure in wildlife, we created a systematic map and bibliographic analysis of the literature. We followed our protocol to conduct a systematic literature search on Scopus, Web of Science and five other databases. In two steps (title/abstract/keywords and full‐text), we screened peer‐reviewed empirical articles, preprints and theses in English that studied the concentration of at least one of 34 PFAS compounds in free‐ranging wildlife or their parts/products. Following the protocol, we extracted data and performed a critical appraisal. We included 581 publications. From the first and only paper in 2001, there was a linear annual increase to 54 papers in 2021. While PFOS (97% of studies), PFOA (91%) and long‐chain PFAS in general were the most measured, few studies investigated new‐generation PFAS (e.g. GenX and ADONA). Across the studied 1042 species from 26 taxonomic classes, the most frequent were the common carp ( Cyprinus carpio , 8%), polar bear ( Ursus maritimus , 6%) and European perch ( Perca fluviatilis , 5%). Most sampling took place in the United States (17%), Norway (13%), Canada (12%) and China (10%), which were also the main publishing countries. Polar regions attracted significant research interest from countries all around the globe. Aquatic habitats (marine: 31%, freshwater: 28%) of temperate zones were the most common locations for sample collection. We encourage researchers to work towards closing the following gaps: investigating new‐generation PFAS, assessing PFAS in mid‐ and low‐income countries and performing more long‐term studies, especially on invertebrates. We note the recent rise in studies on the physiological consequences of PFAS exposure and encourage further work on this crucial topic. Furthermore, we recommend that the statement of potential and actual conflicts of interest, and the provision of raw data and analysis code should be made compulsory by all journals and routinely enforced. This practice will mitigate conflict of interest and ensure reproducibility.
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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 | 0.037 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.271 | 0.222 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".