MétaCan
Menu
← Back to cohort
Record W2793112063 · doi:10.1002/rcm.8093

Characterization of Athabasca lean oil sands and mixed surficial materials: Comparison of capillary electrophoresis/low‐resolution mass spectrometry and high‐resolution mass spectrometry

2018· article· en· W2793112063 on OpenAlexafffundabout
Matthew S. MacLennan, Kerry M. Peru, Chris Swyngedouw, Ian Fleming, David D. Y. Chen, John V. Headley

Bibliographic record

VenueRapid Communications in Mass Spectrometry · 2018
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of SaskatchewanBP (Canada)Environment and Climate Change CanadaUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryMass spectrometryChromatographyOil sandsCapillary electrophoresisNaphthenic acidOrbitrapSample preparationElectrospray ionizationResolution (logic)HydrocarbonGas chromatographyAnalytical Chemistry (journal)Capillary electrophoresis–mass spectrometryAsphalt

Abstract

fetched live from OpenAlex

RATIONALE: Oil sands mining in Alberta, Canada, requires removal and stockpiling of considerable volumes of near-surface overburden material. This overburden includes lean oil sands (LOS) which cannot be processed economically but contain sparingly soluble petroleum hydrocarbons and naphthenic acids, which can leach into environmental waters. In order to measure and track the leaching of dissolved constituents and distinguish industrially derived organics from naturally occurring organics in local waters, practical methods were developed for characterizing multiple sources of contaminated water leakage. METHODS: Capillary electrophoresis/positive-ion electrospray ionization low-resolution time-of-flight mass spectrometry (CE/LRMS), high-resolution negative-ion electrospray ionization Orbitrap mass spectrometry (HRMS) and conventional gas chromatography/flame ionization detection (GC/FID) were used to characterize porewater samples collected from within Athabasca LOS and mixed surficial materials. GC/FID was used to measure total petroleum hydrocarbon and HRMS was used to measure total naphthenic acid fraction components (NAFCs). HRMS and CE/LRMS were used to characterize samples according to source. RESULTS: The amounts of total petroleum hydrocarbon in each sample as measured by GC/FID ranged from 0.1 to 15.1 mg/L while the amounts of NAFCs as measured by HRMS ranged from 5.3 to 82.3 mg/L. Factors analysis (FA) on HRMS data visually demonstrated clustering according to sample source and was correlated to molecular formula. LRMS coupled to capillary electrophoresis separation (CE/LRMS) provides important information on NAFC isomers by adding analyte migration time data to m/z and peak intensity. CONCLUSIONS: is a possible tracer for LOS seepage. CE/LRMS provides complementary information and is a feasible and practical option for source evaluation of NAFCs in water.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.077
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.015
GPT teacher head0.263
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 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

Citations6
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueRapid Communications in Mass Spectrometry→Same topicPetroleum Processing and Analysis→French-language works237,207→