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Record W2896024222 · doi:10.1007/s11356-018-3403-2

Variability of release rate of flame retardants in wastewater treatment plants

2018· article· en· W2896024222 on OpenAlexafffund
Jesse Shen, Shirley Anne Smyth, Ronald L. Droste, Danaëlle Delâge

Bibliographic record

VenueEnvironmental Science and Pollution Research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversity of OttawaEnvironment and Climate Change Canada
FundersEnvironment and Climate Change Canada
KeywordsBiosolidsEffluentEnvironmental scienceWastewaterEnvironmental chemistrySewage treatmentEcotoxicologyFraction (chemistry)ChemistryEnvironmental engineeringChromatography

Abstract

fetched live from OpenAlex

Information on variability is important in the assessment of the releases and potential risks of brominated flame retardants (BRFs) in the environment, but related data are limited. In this study, two release-characterizing parameters, release fraction to final effluent and influent-biosolids transfer coefficient, were used to quantify releases of five BFRs from eight secondary wastewater treatment plants (WWTPs). The five BFRs are recalcitrant, hydrophobic, and low in volatility. The two parameters for these BFRs were found to vary from day to day and season to season within individual WWTPs as well as from one WWTP to another. These temporal and spatial variations were, however, comparable to each other and both within a factor of 3 above or below the parameter averages. Averages for release fraction were in the range of 0.02-0.29 and those for influent-biosolids transfer coefficient in the range of 3-26 L/g, depending upon a given BFR at a given WWTP. These ranges and the observed factor-3 variability are not only useful for estimating releases of the five BFRs, but more importantly provide read-across data for the assessment of substances with similar physical-chemical properties.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.006
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.305
Teacher spread0.276 · 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 designBench or experimental
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

Citations11
Published2018
Admission routes2
Has abstractyes

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