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Record W3131520007 · doi:10.1111/gwmr.12435

A New Method for Determining Compound Specific Carbon Isotope of Chlorinated Solvents in Porewater

2021· article· en· W3131520007 on OpenAlexaff
Jofre Herrero, Diana Puigserver, Beth L. Parker, José M. Carmona

Bibliographic record

VenueGroundwater Monitoring & Remediation · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsChemistryTrichloroethyleneTetrachloroethyleneDetection limitMass spectrometryEnvironmental chemistryExtraction (chemistry)Gas chromatographyChromatographySolventGas chromatography–mass spectrometrySolid phase extractionIsotope-ratio mass spectrometryIsotope analysisContaminationIsotopes of carbonTotal organic carbonOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A new method for the extraction of chlorinated solvents (CSs) from porewater with dimethylacetamide (DMA) used as a solvent and the determination of δ 13 C by gas chromatography‐isotope ratio mass spectrometry (GC‐IRMS) with solid‐phase microextraction (SPME) are presented. This method was used for the determination of δ 13 C of chloroethenes and chloromethanes. The extraction of the CSs from porewater with DMA led to a minimal loss of mass of solvent and chlorinated compounds. The accuracy of the method was verified with the analysis of the pure injected compounds using elemental analyzer—isotope ratio mass spectrometry (EA‐IRMS). It has been effectively applied in a study area in saturated soil samples of a pollutant source zone of perchloroethylene (PCE) and trichloroethylene (TCE). The limit of quantification of the new method was 0.034 μg/g for PCE and TCE for 10–20 g of soil sample. This new method allows for compound‐specific isotope analysis of CSs in porewater, which can be beneficial in sites where the identification of contamination sources and the behavior of the contaminants are not clear.

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 score0.233
Threshold uncertainty score0.688

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)0.0000.000

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 head0.025
GPT teacher head0.283
Teacher spread0.257 · 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
Published2021
Admission routes1
Has abstractyes

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