Effects of persistent organic pollutants on environment, health and mountains: a review
Bibliographic record
Abstract
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".