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Record W4379382936 · doi:10.1016/j.envint.2023.108020

Toxic effects and toxicological mechanisms of chlorinated paraffins: A review for insight into species sensitivity and toxicity difference

2023· review· en· W4379382936 on OpenAlexaff
Shuangshuang Chen, Yufeng Gong, Yun Luo, Rong Cao, Jiajia Yang, Lin Cheng, Yuan Gao, Haijun Zhang, Jiping Chen, Ningbo Geng

Bibliographic record

VenueEnvironment International · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsChlorinated paraffinsToxicityEnvironmental toxicologyEnvironmental chemistryToxicologyChemistryBiology

Abstract

fetched live from OpenAlex

Chlorinated paraffins (CPs), a group of chlorinated alkane mixtures, are frequently detected in various environmental matrices and human bodies. Recently, CPs have garnered considerable attention owing to their potential to induce health hazards in wildlife and human. Several reviews have discussed short-chain CPs (SCCPs) induced ecological risk; however, a comprehensive understanding of the underlying toxic mechanisms and a comparison among SCCPs, medium-, and long-chain CPs (MCCPs and LCCPs, respectively) are yet to be established. This review summarizes the latest research progress on the toxic effects and the underlying molecular mechanisms of CPs. The main toxicity mechanisms of CPs include activation of several receptors, oxidative stress, disturbance of energy metabolism, and inhibition of gap junction-mediated communication. The sensitivity of different species to CP-mediated toxicities varies markedly, with aquatic organisms exhibiting the highest sensitivity to CP-induced toxicity. The toxicity comparison analysis indicated that MCCPs may be unsafe as potential substitutes for SCCPs.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.061
GPT teacher head0.317
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations72
Published2023
Admission routes1
Has abstractyes

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