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Record W2955689138 · doi:10.1016/j.envint.2019.104944

Nontarget profiling of organic compounds in a temporal series of hydraulic fracturing flowback and produced waters

2019· article· en· W2955689138 on OpenAlexafffund
Chenxing Sun, Yifeng Zhang, Daniel S. Alessi, Jonathan W. Martin

Bibliographic record

VenueEnvironment International · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryMass spectrometryPhthalateEnvironmental chemistryChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

O-), among which a series of aldehydes was proposed as degradation products of polyethylene glycols, and two series of alkyl ethoxylate carboxylates could be proprietary HF additives. Many other ions were confidently assigned a formula by accurate mass measurement and were subsequently prioritized for identification by matching to records in ChemSpider and the US EPA's CompTox Chemistry Dashboard. Quaternary ammonium compounds, amine oxides, organophosphorous compounds, phthalate diesters and hydroxyquinoline were identified with high confidence by MS/MS spectra (Level 3), matching to reference spectra in MassBank (Level 2) or to authentic standards (Level 1). Temporal trends showed that most of the compounds declined in abundance over the first nine days of flowback, except for phthalate diesters and hydroxyquinoline that were still observed on Day 30 and had disappearance half-lives of 61 and 91 days, respectively. All the compounds followed first-order disappearance kinetics in flowback, except for polyoxygenated acids which followed second-order kinetics. This analysis and the workflow, based largely on public on-line databases, enabled profiling of complex organic compounds in HF-FPW, and will likely be useful for further understanding the toxicity and chemical fate of HF-FPW.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.999

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)0.0020.000

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 head0.179
Teacher spread0.175 · 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 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

Citations49
Published2019
Admission routes2
Has abstractyes

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