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Record W1980263193 · doi:10.1039/c3em00089c

Determination of thermodynamic and transport parameters of naphthenic acids and organic process chemicals in oil sand tailings pond water

2013· article· en· W1980263193 on OpenAlexaff
Xiaomeng Wang, Lisa A. Robinson, Qing Wen, Kim L. Kasperski

Bibliographic record

VenueEnvironmental Science Processes & Impacts · 2013
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsTailingsOil sandsGroundwaterEnvironmental scienceEnvironmental chemistryChemistryEnvironmental engineeringGeologyGeotechnical engineeringMaterials science

Abstract

fetched live from OpenAlex

Oil sand tailings pond water contains naphthenic acids and process chemicals (e.g., alkyl sulphates, quaternary ammonium compounds, and alkylphenol ethoxylates). These chemicals are toxic and can seep through the foundation of the tailings pond to the subsurface, potentially affecting the quality of groundwater. As a result, it is important to measure the thermodynamic and transport parameters of these chemicals in order to study the transport behavior of contaminants through the foundation as well as underground. In this study, batch adsorption studies and column experiments were performed. It was found that the transport parameters of these chemicals are related to their molecular structures and other properties. The computer program (CXTFIT) was used to further evaluate the transport process in the column experiments. The results from this study show that the transport of naphthenic acids in a glass column is an equilibrium process while the transport of process chemicals seems to be a non-equilibrium process. At the end of this paper we present a real-world case study in which the transport of the contaminants through the foundation of an external tailings pond is calculated using the lab-measured data. The results show that long-term groundwater monitoring of contaminant transport at the oil sand mining site may be necessary to avoid chemicals from reaching any nearby receptors.

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 score0.043
Threshold uncertainty score0.486

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.004
GPT teacher head0.210
Teacher spread0.207 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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