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Record W4318218874 · doi:10.1680/jenes.22.00089

Effects of persistent organic pollutants on environment, health and mountains: a review

2023· review· en· W4318218874 on OpenAlexvenueno aff
Gervas E. Assey, Emmanuel Mogusu

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsnot available
Fundersnot available
KeywordsFood chainPollutantBioaccumulationEnvironmental scienceEnvironmental chemistryVolatility (finance)Human healthEcologyChemistryBiologyEnvironmental healthBusiness

Abstract

fetched live from OpenAlex

Persistent organic pollutants (POPs) are chemicals that persist in the environment, bioaccumulate through the food chain and can exhibit toxicity with threatening effects to the environment, humans and animals. Due to their volatility and semi-volatility, they are found in air and they have the potential to be transported to high-altitude mountain locations as well as to high-latitude areas. The objectives of this study are; firstly, to look into the environmental and health impacts of POPs; secondly, to look into studies that covered the transport of POPs through atmospheric air, water and other POP sources such as sediments and soils; and, thirdly, to look into possible solutions that have been put forward regarding POP removal from the environment. The methodology that was used to look for references was through the Google, PubMed, Google Scholar and ResearchGate. The initial search retrieved 63 peer-reviewed articles and abstracts. The studies revealed that, POPs remain in the environment, causing health effects through their toxicity. Humans and animals take in POPs through the food chain because of the lipophilic nature of these compounds. Solutions that have been put forward by the studies are stopping the use of POPs, substituting POPs with non-toxic chemical compounds and having a wide range of non-chemical alternatives.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.255
Teacher spread0.240 · 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 designOther design
Domainnot available
GenreReview

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

Citations17
Published2023
Admission routes1
Has abstractyes

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