Research Progress in Current and Emerging Issues of PFASs’ Global Impact: Long-Term Health Effects and Governance of Food Systems
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFASs) are found everywhere, including food, cosmetics, and pharmaceuticals. This review introduces PFASs comprehensively, discussing their nature and identifying their interconnection with microplastics and their impacts on public health and the environment. The human cost of decades of delay, cover-ups, and mismanagement of PFASs and plastic waste is outlined and briefly explained. Following that, PFASs and long-term health effects are critically assessed. Risk assessment is then critically reviewed, mentioning different tools and models. Scientific research and health impacts in the United States of America are critically analyzed, taking into consideration the Center for Disease Control (CDC)'s PFAS Medical Studies and Guidelines. PFAS impact and activities studies around the world have focused on PFAS levels in food products and dietary intake in different countries such as China, European countries, USA and Australia. Moreover, PFASs in drinking water and food are outlined with regard to risks, mitigation, and regulatory needs, taking into account chemical contaminants in food and their impact on health and safety. Finally, PFAS impact and activities briefings specific to regions around the world are discussed, referring to Australia, Vietnam, Canada, Europe, the United States of America (USA), South America, and Africa. The PFAS crisis is a multifaceted issue, exacerbated by mismanagement, and it is discussed in the context of applying the following problem-solving analytical tools: the Domino Effect Model of accident causation, the Swiss Cheese Theory Model, and the Ishikawa Fish Bone Root Cause Analysis. Last but not least, PFASs' impacts on the Sustainable Development Goals (SDGs) of 2030 are rigorously discussed.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".