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On the occurrence, behaviour, and fate of naphthenic acid fraction compounds in aquatic environments

2025· review· en· W4406204146 on OpenAlexafffundabout
Ian J. Vander Meulen, John V. Headley, Dena W. McMartin

Bibliographic record

VenueThe Science of The Total Environment · 2025
Typereview
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of LethbridgeUniversity of SaskatchewanEnvironment and Climate Change Canada
FundersOffice of Energy Research and DevelopmentNatural Resources Canada
KeywordsNaphthenic acidFraction (chemistry)ChemistryEnvironmental chemistryChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Naphthenic acids and naphthenic acid fraction compounds (NAFCs) are associated with production of unconventional petroleum resources, especially the Athabasca Oil Sands of Alberta, Canada. This complex mixture of acidic organic compounds is toxic to a variety of taxa, and so represents an important environmental management challenge. Thus, there is clear motivation to better understand the occurrence and characteristics of NAFCs in aquatic environments, their chemical behaviour, and environmental fate. Empowered by modern high-resolution mass spectrometry analyses, improved descriptions of the environmental occurrence of NAFCs have emerged. These studies include spatiotemporal survey studies describing the characteristics and quantities of NAFCs, as well as forensic methods working towards reliable source differentiations. Work has also proceeded in earnest to advance mechanistic understandings of how NAFCs are affected by passive phenomena, such as soil and sediment sorption, and chemically reactive mechanisms such as photolysis and biodegradation. Further advances describe the environmental fate and behaviour of NAFCs as they are transported and transformed across environmental compartments. In the context of Canadian oil sands, the available data describe NAFCs as a dynamic compound class that both affects and is affected by their receiving environment. By working towards a comprehensive understanding of the behaviour and fate of NAs and NAFCs, we might better anticipate the extent to which residual toxic effects may persist in reclaimed landscapes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.184
Threshold uncertainty score0.366

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.018
GPT teacher head0.265
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2025
Admission routes3
Has abstractyes

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