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Record W2469800175 · doi:10.2134/jeq2016.01.0017

Influence of Acidification on the Partitioning of Steroid Hormones among Filtrate, Filter Media, and Retained Particulate Matter

2016· article· en· W2469800175 on OpenAlexaff
Sonya M. Havens, Curtis J. Hedman, Jocelyn D.C. Hemming, Mark Mieritz, Martin M. Shafer, James J. Schauer

Bibliographic record

VenueJournal of Environmental Quality · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsInternational Institute for Sustainable Development
FundersU.S. Environmental Protection Agency
KeywordsChemistryParticulatesEnvironmental chemistryHormoneChromatographyOrganic matterFilter (signal processing)BiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Hormone contamination of aquatic systems has been shown to have deleterious effects on aquatic biota. However, the assessment of hormone contamination of aquatic environments requires a quantitative evaluation of the potential effects of sample preservation on hormone concentrations. This study investigated the influence of acidification (pH 2) of surface water samples on the partitioning of hormones among filtrate, filter media, and filter‐retained particulate matter. Hormones were spiked into unpreserved and sulfuric acid–preserved ultrapure water and surface water runoff samples. The samples were filtered, and hormones were extracted from the filter and filtrate and analyzed by high‐performance liquid chromatography. Acidification did not influence the partitioning of hormones onto the filter media. For the majority of the hormones investigated in this study, the partitioning of hormones to the filter‐retained particulate matter was not influenced by acidification. Acidification increased the partitioning of progesterone and melengestrol acetate onto the retained particulate matter (about 25% for both analytes). Incorporation of an isotopically labeled internal standard (ISTD) for progesterone accounted for the loss of progesterone to the filter‐retained particulates and resulted in accurate concentrations of progesterone in the filtrate. The incorporation of an ISTD for melengestrol acetate, however, was unable to account for the loss of melengestrol acetate to the retained particulates and resulted in underestimations of melengestrol acetate in the filtrate. Our results indicate that the analysis of melengestrol acetate in acid preserved surface runoff samples should be conducted on the filter‐retained particulates as well as the filtrate. Core Ideas Acidification did not influence the partitioning of hormones onto the filter media. Acidification increased partitioning of progesterone to particulates. Acidification increased partitioning of melengestrol acetate to particulates. Internal standard accounted for partitioning of progesterone to particulates. Internal standard did not account for partitioning of melengestrol acetate to particulates.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score1.000

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 studies0.0000.001
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 head0.031
GPT teacher head0.273
Teacher spread0.241 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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