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Record W4226181108 · doi:10.1002/etc.5340

Interlaboratory Comparison of a Biomimetic Extraction Method Applied to Oil Sands Process–Affected Waters

2022· article· en· W4226181108 on OpenAlexafffund
Daniel J. Letinski, Asfaw Bekele, Martin J. Connelly

Bibliographic record

VenueEnvironmental Toxicology and Chemistry · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsImperial Oil (Canada)
FundersImperial Oil LimitedSyncrudeSuncor Energy IncorporatedCanada's Oil Sands Innovation AllianceExxon Mobil Corporation
KeywordsExtraction (chemistry)Environmental scienceRound robin testSample preparationChromatographySolid phase extractionSolid-phase microextractionProduced waterPetroleumEnvironmental chemistryChemistryPulp and paper industryGas chromatography–mass spectrometryEnvironmental engineeringMass spectrometryMathematicsEngineering

Abstract

fetched live from OpenAlex

Biomimetic extraction using solid-phase microextraction is a passive sampling analytical method that can predict the aquatic toxicity of complex petroleum substances. The method provides a nonanimal alternative to traditional bioassays with the potential to reduce both vertebrate and invertebrate aquatic toxicity testing. The technique uses commercially available polydimethylsiloxane-coated fibers that, following nondepletive extraction of water samples, are injected into a gas chromatograph with flame ionization detection. As the predictive nature of the method is operationally defined, it is critical that its application be harmonized with regard to extraction, analysis, and standardization parameters. Results are presented from a round robin program comparing the results from 10 laboratories analyzing four different sample sets of dissolved organics in water. Samples included two incurred oil sands process-affected waters and a cracked gas oil water accommodated fraction. A fourth sample of cracked gas oil blended in an oil sands process-affected water was analyzed to demonstrate the method's ability to differentiate between neutral and ionizable dissolved hydrocarbons. Six of the 10 laboratories applied an automated version of the method using a robotic autosampler where the critical extraction steps are precisely controlled and which permits batch screening of water samples for aquatic toxicity potential. The remaining four laboratories performed the solid-phase microextraction manually. The automated method demonstrated good reproducibility with between-laboratory variability across the six laboratories and four samples yielding a mean relative standard deviation of 14%. The corresponding between-laboratory variability across the four laboratories applying the manual extraction was 53%, demonstrating the importance of precisely controlling the extraction procedure. Environ Toxicol Chem 2022;41:1613-1622. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

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)0.013
metaresearch head score (Gemma)0.025
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 score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.268
Teacher spread0.262 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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