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Record W2802822182 · doi:10.1139/facets-2017-0115

Nickel geochemistry of oil sands fluid petroleum coke deposits, Alberta, Canada

2018· article· en· W2802822182 on OpenAlexaffvenueabout
Jake A. Nesbitt, Jared Robertson, Lawrence A. Swerhone, Matthew B.J. Lindsay

Bibliographic record

VenueFACETS · 2018
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPetroleum cokeSorptionOil sandsCokeNickelLeaching (pedology)Environmental chemistryDesorptionDissolutionChemistrySulfateGeologyPetroleumMineralogyAdsorptionMaterials scienceSoil water

Abstract

fetched live from OpenAlex

Nickel (Ni) leaching from oil sands petroleum coke can have toxicological effects on aquatic organisms. However, geochemical controls on Ni release, transport, and attenuation within coke deposits remains limited. We examined the geochemistry of fluid coke and associated pore waters from two deposits at an oil sands mine near Fort McMurray, Alberta, Canada. Synchrotron-based micro-X-ray fluorescence (μXRF) and micro-X-ray absorption near edge structure (μXANES) spectroscopy show that Ni(II)-porphyrin complexes dominate, but inorganic phases including Ni(II)-sulfide and Ni(II)-oxide comprise a minor component of fluid coke. Sequential chemical extractions suggested that sorption–desorption reactions may influence Ni mobility within fluid coke deposits. Although only a small proportion of total Ni (<4%) is susceptible to leaching under environmentally relevant concentrations, dissolved Ni concentrations ( n = 65) range from 2 to 120 μg·L −1 (median 7.8 μg·L −1 ) within the two deposits and generally decrease with depth below the water table. Pore water Ni concentrations are negatively correlated with pH, but not with dissolved sulfate, bicarbonate, or chloride. Overall, our findings suggest that pore water pH and sorption–desorption reactions are principal controls on dissolved Ni concentrations within oil sands fluid petroleum coke deposits.

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 categoriesInsufficient payload (model declined to judge)
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 score0.099
Threshold uncertainty score1.000

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)0.0010.000

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 head0.005
GPT teacher head0.209
Teacher spread0.204 · 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 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

Citations7
Published2018
Admission routes3
Has abstractyes

Explore more

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