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Record W1975704882 · doi:10.1080/10807039.2014.975003

Distribution Characteristics and Risk Assessments of PAHs in Fish from Lake Taihu, China

2014· article· en· W1975704882 on OpenAlexaff
Hailei Su, Fengchang Wu, Jianyang Guo, Yan Hu, Chenglian Feng, Cheng Chen, John P. Giesy

Bibliographic record

VenueHuman and Ecological Risk Assessment An International Journal · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBighead carpCrucian carpHuman healthEnvironmental scienceCarpHealth riskFish <Actinopterygii>Environmental chemistryCommon carpBiotaFisheryAquatic ecosystemEcologyBiologyChemistryCyprinusEnvironmental health

Abstract

fetched live from OpenAlex

The concentrations of PAHs in four species of fish (Common carp, Crucian carp, Bighead carp, and Topmouth culter) from Lake Taihu were tested, and the human health risks of PAHs by fish consumption were evaluated. Results showed that concentrations of PAHs in fish from Lake Taihu were 52.5–247.6 ng/g wet weight (ww), and the BaP equivalent concentrations of total PAHs (B[a]Peq) were 0.2–0.6 ng/g ww, which were less than the screening value of 2.6 ng/g wet for human consumption. The concentration sequences of PAHs in fish from Lake Taihu from high to low were Bighead carp > Crucian carp > Common carp > Topmouth culter. The human health risk level of PAHs by fish consumption was 5.8 ± 2.5 × 10−6, which was less than the maximum acceptable risk level of 1 × 10−5 for human health set by the U.S. Environmental Protection Agency. The tissue residue guideline (TRG) of PAHs for protecting aquatic wildlife was 1.3 mg/kg diet ww, which was higher than the concentrations of PAHs in fish from Lake Taihu. The results indicated that fish consumption from Lake Taihu would not cause health risk or harmful effects on wildlife that consume aquatic biota.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0030.000

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 head0.012
GPT teacher head0.300
Teacher spread0.288 · 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 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

Citations15
Published2014
Admission routes1
Has abstractyes

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