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Record W2010582258 · doi:10.5539/ep.v4n2p24

Influence of Habitat Pollution on Organophosphate Esters and Polycyclic Aromatic Hydrocarbons in Cicadas

2015· article· en· W2010582258 on OpenAlexvenueno aff
Haruki Shimazu

Bibliographic record

VenueEnvironment and Pollution · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsnot available
FundersKindai University
KeywordsOrganophosphateEnvironmental chemistryPollutantSoil waterEcotoxicologyPollutionContaminationChemistryPesticideEnvironmental scienceEcologySoil scienceBiology

Abstract

fetched live from OpenAlex

The present study examines the concentration levels of seven organophosphate esters (OPEs) and nine polycyclic aromatic hydrocarbons (PAHs) in cicadas and the influence factors of their habitats on the contamination of cicadas. Adult cicadas, nymphal exoskeletons, soils, and saps were sampled in Japan. The total concentrations of seven OPEs and nine PAHs for the adult cicadas ranged from 107 to 8940 ng/g-dw and from 58.9 to 1580 ng/g-dw, respectively. Some OPEs and PAHs were detected in heads, thoraxes, and abdomens of the adult cicadas. The concentrations were higher in the heads than in the other parts. The relationship between OPEs and PAHs in soils and those in cicadas was considerably positive. For some OPEs and PAHs, the concentrations in the saps tend to be higher as those in the cicadas increase. These tendencies indicate that cicadas intake OPEs and PAHs from soils and saps contaminated with these pollutants. The concentrations of OPEs and PAHs tend to increase with those in the atmospheric depositions. This probably shows that OPEs and PAHs in atmospheric depositions pollute soils, and cicadas intake the pollutants from the contaminated soils and saps.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

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)0.0000.000

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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