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Record W2030466622 · doi:10.1021/es049044o

Mass Budget of Perfluorooctane Surfactants in Lake Ontario

2004· article· en· W2030466622 on OpenAlexaboutno aff
Bryan Boulanger, Aaron M. Peck, Jerald L. Schnoor, Keri C. Hornbuckle

Bibliographic record

VenueEnvironmental Science & Technology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsnot available
FundersNational Institute of Environmental Health SciencesNational Institutes of HealthU.S. Environmental Protection Agency
KeywordsPerfluorooctaneEnvironmental scienceParticulatesBiotaEnvironmental chemistryHydrology (agriculture)Environmental engineeringChemistryEcologyGeologySulfonateBiology

Abstract

fetched live from OpenAlex

Perfluorooctane surfactants have been reported in biota, water, and air samples worldwide. Despite these reports, the main environmental sources of these compounds remain undefined. To address this gap in knowledge, an annual lake-wide mass budget of eight perfluorooctane surfactants was developed for Lake Ontario. To determine the atmospheric contribution to the mass budget, over-the-lake gas-phase air concentrations for N-EtFOSE and N-EtFOSA and particulate-phase air concentrations for PFOS in any air sample are reported for the first time, with mean concentrations when present of 0.5+/-0.32 (N-EtFOSE gas-phase), 1.1+/-0.9 (N-EtFOSA gas-phase), and 6.4+/-3.3 (PFOS particulate-phase) pg/m3. The mass budget finds inflow from Lake Erie (14 361+/-4489 kg sigma perfluorooctane surfactants) and wastewater discharge (1762+/-2697 kg sigma perfluorooctane surfactants) to be the major sources, while outflow through the St. Lawrence River is the dominant loss mechanism (22,727+/-7060 kg/year sigma perfluorooctane surfactants). Using the mass budget data, the steady state and measured mean concentrations in the lake water are the same at the 95% confidence level.

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, Insufficient 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 score0.164
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
Bibliometrics0.0000.002
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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

Citations145
Published2004
Admission routes1
Has abstractyes

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