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Record W4396956473 · doi:10.1021/acs.est.3c10098

The Ins and Outs of Per- and Polyfluoroalkyl Substances in the Great Lakes: The Role of Atmospheric Deposition

2024· article· en· W4396956473 on OpenAlexaffabout
Chunjie Xia, Staci L. Capozzi, Kevin Romanak, Daniel C. Lehman, Alice Dove, Violeta Richardson, T. A. Greenberg, Daryl J. McGoldrick, Marta Venier

Bibliographic record

VenueEnvironmental Science & Technology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsEnvironment and Climate Change Canada
FundersEnvironmental Protection Agency
KeywordsDeposition (geology)Environmental chemistryEnvironmental scienceAcid depositionAtmospheric sciencesChemistryGeologySedimentSoil scienceGeomorphologySoil water

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide As part of the Integrated Atmospheric Deposition Network, precipitation ( n = 207) and air ( n = 60) from five sites and water samples ( n = 87) from all five Great Lakes were collected in 2021–2023 and analyzed for 41 per- and polyfluoroalkyl substances (PFAS). These measurements were combined with other available data to estimate the mass budget for four representative compounds, PFBA, PFBS, PFOS, and PFOA for the basin. The median Σ 41 PFAS concentrations in precipitation across the five sites ranged between 2.4 and 4.5 ng/L. The median Σ 41 PFAS concentration in lake water was highest in Lake Ontario (11 ng/L) and lowest in Lake Superior (1.3 ng/L). The median Σ 41 PFAS concentration in air samples was highest in Cleveland at 410 pg/m 3 and lowest at Sleeping Bear Dunes at 146 pg/m 3 . The net mass transfer flows were generally negative for Lakes Superior, Michigan, and Huron and positive for Lakes Erie and Ontario, indicating that the three most northern lakes are accumulating PFAS and the other two are eliminating PFAS. Atmospheric deposition is an important source of PFAS, particularly for Lake Superior.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.008
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.004
GPT teacher head0.222
Teacher spread0.218 · 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

Citations51
Published2024
Admission routes2
Has abstractyes

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