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Record W6996621595

Scientifc Strategy for PFAS Analysis and Bioremediation at UBFC (2023-2033)

2023· article· en· W6996621595 on OpenAlexaboutno aff

Bibliographic record

VenueCherry (Univesrity of Belgrade, Faculty of Chemistry) · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsnot available
Fundersnot available
KeywordsHuman healthScale (ratio)Developing countryGlobal healthPrecautionary principleHealth risk
DOInot available

Abstract

fetched live from OpenAlex

Per- and polyfluoroalkyl substances (PFAS) are a group of synthetic chemicals that have been widely used in various industrial and consumer products due to their unique properties, such as oil and water repellency, thermal stability, and durability. However, the persistence and mobility of these chemicals in the environment have raised concerns about their potential adverse effects on human health and the environment. PFAS have been detected on a global scale in various environmental media, such as soil, water, air, and biota. As a country undergoing economic development and transition, Serbia faces the challenge of managing and addressing the PFAS contamination in its environment. This challenge requires a comprehensive and science-based strategy that can effectively reduce the exposure and risks of PFAS to human health and the environment. This document aims to provide a scientific strategy for solving the PFAS challenge in Serbia. Firstly, it is important to acknowledge that PFAS are persistent and bioaccumulative in the environment, which means that they do not break down easily and can accumulate in the food chain, posing a long-term risk to human health and the environment. Therefore, a precautionary approach is necessary to minimize the exposure and risks of PFAS. International experience and cooperation are very important for developing an effective scientifc strategy for addressing the PFAS challenge in Serbia. PFAS are a global issue, and many countries have already implemented measures to manage and reduce the exposure and risks of PFAS. Therefore, it is important to draw on international experience and best practices when developing the strategy for Serbia. International experience can provide valuable insights into the sources, pathways, and fate of PFAS, as well as the e"ectiveness of various risk management measures. For example, the United States, Canada, and some European countries have established regulatory frameworks for PFAS, which can serve as a model for Serbia. Other countries have implemented remediation measures for contaminated sites, which can provide valuable insights for selecting appropriate remediation technologies in Serbia. Moreover, international experience can provide access to the latest scientific knowledge, methods, and technologies for assessing and managing PFAS contamination. For example, international organizations such as the United Nations Environment Programme (UNEP) and the Organization for Economic Co-operation and Development (OECD) have developed guidance documents and tools for assessing and managing PFAS contamination. The scientific strategy for solving the PFAS challenge in Serbia is based on a thorough understanding of the sources, pathways, and fate of PFAS in the environment. It is also based on a comprehensive and systematic approach, including risk assessment, monitoring, regulation, remediation, and communication. This strategy is tailored to the specific context and needs of Serbia. It is based on the latest scientifc knowledge and practical experience from other countries and regions, focusing on the European Union, USA, China and Japan. The cultural, social, economic, and political factors can affect the implementation and effectiveness of the strategy, and therefore, the strategy is developed through a collaborative and participatory process involving stakeholders from different sectors and levels. The international experience provided helpful guidance and lessons learned, but ultimately, the strategy is based on local knowledge, priorities, and capacities. By implementing this strategy, we aim to contribute to that Serbia can protect its citizens and environment from the potential harm of PFAS and achieve sustainable development.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.042
GPT teacher head0.306
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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