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Record W4414756074 · doi:10.1021/acsestwater.5c01023

Early Mass Transfer of Monocyclic Aromatics to Water Following Diluted Bitumen Spills in Freshwater Limnocorrals

2025· article· en· W4414756074 on OpenAlexafffundabout
Sawyer S. Stoyanovich, L. Saunders, Anca-Maria Tugulea, Joan Hnatiw, R. Strathern, Mark L. Hanson, Bruce P. Hollebone, Diane M. Orihel, Vince Palace, José Luis Rodríguez‐Gil, Jules M. Blais

Bibliographic record

VenueACS ES&T Water · 2025
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsInternational Institute for Sustainable DevelopmentQueen's UniversityEnvironment and Climate Change CanadaUniversity of ManitobaHealth CanadaUniversity of Ottawa
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaTransport Canada
KeywordsBTEXDissolutionEvaporationContaminationAsphaltVolatilisationWater pollutionOil spill

Abstract

fetched live from OpenAlex

During the initial days of a diluted bitumen (dilbit) spill, the monocyclic aromatic hydrocarbons benzene, toluene, ethylbenzene, and o, m, p -xylenes (BTEX) rapidly partition into air and water. Accurately characterizing the evaporation and dissolution trends of BTEX is essential for assessing potential environmental exposures. We investigated these processes following dilbit spills into limnocorrals in a boreal lake in Northwestern Ontario, with spill volumes ranging from 1.5 to 180 L, resulting in oil-to-water ratios from 1:71,000 to 1:500. BTEX concentrations in the water column peaked within 10–24 h and dropped below detection limits after 96 h. A mass transfer model, parametrized based on physical–chemical properties of the individual BTEX compounds and environmental conditions, accurately predicted BTEX fractionation and dissolution trends, with 29–86% of predictions within 3-fold of measured concentrations across all spill scenarios. This is the first report on the dissolution trends of BTEX following large-scale experimental dilbit spills in natural freshwater systems, which allowed for an important field evaluation of the model at a variety of different dilbit spill sizes.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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 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

Citations0
Published2025
Admission routes3
Has abstractyes

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