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Record W2994931919 · doi:10.1073/pnas.1915322116

Persistent, bioaccumulative, and toxic properties of liquid crystal monomers and their detection in indoor residential dust

2019· article· en· W2994931919 on OpenAlexaff
Huijun Su, Shaobo Shi, Ming Zhu, Doug Crump, Robert J. Letcher, John P. Giesy, Guanyong Su

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversity of SaskatchewanCarleton UniversityEnvironment and Climate Change Canada
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsEnvironmental chemistryContaminationChemistryBioaccumulationTranscriptomeBiologyGeneGene expressionBiochemistryEcology

Abstract

fetched live from OpenAlex

, was observed in vitro. Modulation of expressions of mRNAs coding for these genes has frequently been reported for toxic (T) persistent organic pollutants (POPs). In LCM mixtures, 33 individual LCMs were identified by use of mass spectrometry and screened for in 53 samples of dust from indoor environments. LCMs were detectable in 47% of analyzed samples, and 17 of the 33 LCMs were detectable in at least 1 sample of dust. Based on chemical properties, including P&B&T of LCMs and their ubiquitous detection in dust samples, the initial screening information suggests a need for studies to determine status and trends in concentrations of LCMs in various environmental matrices as well as tissues of humans and wildlife. There is also a need for more comprehensive in vivo studies to determine toxic effects and potencies of LCMs during chronic, sublethal exposures.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.029
GPT teacher head0.249
Teacher spread0.220 · 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 designObservational
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

Citations182
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicToxic Organic Pollutants ImpactFrench-language works237,207