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Record W7108201254 · doi:10.1021/acsestwater.5c00692

Ten Thousand Years of Bitumen and Polycyclic Aromatic Compound Transport in the Athabasca River, Canada

2025· article· en· W7108201254 on OpenAlexafffundabout

Bibliographic record

VenueACS ES&T Water · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsEnvironment and Climate Change CanadaGovernment of AlbertaUniversity of Alberta
FundersGovernment of Alberta
KeywordsOil sandsAsphaltSedimentErosionHydrology (agriculture)Crude oil

Abstract

fetched live from OpenAlex

Polycyclic aromatic compounds (PACs) are a key contaminant of concern within the oil sands region of Alberta, Canada. Inputs of PACs to the Athabasca River have the potential to negatively affect aquatic ecosystems, including the downstream Peace-Athabasca Delta; however, quantitative identification of both natural and anthropogenic PAC inputs to the Athabasca River remains elusive. Here, we quantify the concentration and composition of PACs in Big Bend, a ∼4 km long and 18 m high cutbank rich in reworked bitumen-cemented unconsolidated sediments. Big Bend sediment ΣPAC concentrations exceeded 188,000 ng/g and were dominated by alkylated species, yielding a PAC compositional profile that differs subtly from McMurray Formation bitumen. A comparison of the PAC composition of Big Bend with other source materials and with 961 measures of Athabasca River water reveals an increase both in PAC concentration and in the relative input of petrogenic PACs from upstream to downstream of the bitumen-bearing McMurray Formation and the oil sands mines. Added to mining inputs is the cutbank erosion of Big Bend, which supplies on average ∼410 kt of sediment and ∼5.3 tons of PACs annually to the Athabasca River immediately upstream of the Peace-Athabasca Delta. These inputs supply about 10% of the river sediment load and as much as half of the PACs transported by the Athabasca River.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.005
GPT teacher head0.200
Teacher spread0.196 · 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 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

Citations1
Published2025
Admission routes3
Has abstractyes

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