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Record W2584091116 · doi:10.1016/j.emcon.2017.01.003

Levels and distribution of polybrominated diphenyl ethers in Three Gorges Reservoir, China

2017· article· en· W2584091116 on OpenAlexaff
Jingxian Wang, Yonghong Bi, Bernhard Henkelmann, Zeyu Wang, Gerd Pfister, Karl‐Werner Schramm

Bibliographic record

VenueEmerging contaminants · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversity of Waterloo
FundersBundesministerium für Bildung und Forschung
KeywordsPolybrominated diphenyl ethersEnvironmental chemistryThree gorgesSedimentEnvironmental scienceChemistryPollutantGeologyOrganic chemistryGeotechnical engineering

Abstract

fetched live from OpenAlex

Polybrominated diphenyl ethers (PBDEs) were investigated in water, sediments, suspended sediments and biofilms in Three Gorges Reservoir (TGR), China. Results showed that dissolved bioavailable PBDEs in water of TGR collected with semipermeable membrane device (SPMD)-based virtual organisms (VOs) were very low in the range of n.d. to 811 pg/g lipid and the detected compounds were mainly low molecular BDEs such as BDE-15, 17, 28, 47, 49, 66, 99 and 100. The PBDE levels in the sediment core collected near the dam were also very low in the range of 84–300 pg/g dw and the detected compounds were mainly large molecular BDEs such as BDE-196, 197, 206, 207 and 208. In suspended sediments and biofilms, the levels of PBDEs ranged from 298 to 52,843 pg/g dw and the detected compounds were also mainly large molecular BDEs such as BDE- 196, 197, 201, 203, 206, 207, 208 and 209. The dominant compound was BDE-209 which accounted for more than 90% of the total BDEs. Therefore, large molecular BDEs tended to be attached on fine particles. The vertical profile of BDEs on suspended sediments (SS) showed that SSs in the middle depth of water contained high level of BDE-209. The phenomenon indicated that most of BDE-209 did not settle into the sediment in front of the dam, instead transported further to downstream.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.151
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

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 head0.018
GPT teacher head0.275
Teacher spread0.257 · 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.

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

Citations3
Published2017
Admission routes1
Has abstractyes

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