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Record W2770614554 · doi:10.1186/s12302-017-0128-7

Determination of trace perchlorate in water: a simplified method for the identification of potential interferences

2017· article· en· W2770614554 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnvironmental Sciences Europe · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicChemical Analysis and Environmental Impact
Canadian institutionsnot available
Fundersnot available
KeywordsPerchlorateIon chromatographyChlorideChemistryNitrateSulfateIodideChromatographyMatrix (chemical analysis)Sample preparationEnvironmental chemistryIonInorganic chemistry

Abstract

fetched live from OpenAlex

Perchlorate contamination of water and food poses potential health risks to humans due to the possible interference of perchlorate with the iodide uptake into the thyroid gland. Perchlorate has been found in food and drinking, surface, or swimming pool waters in many countries, including the United States, Canada, France, Germany, and Switzerland, with ion chromatography (IC) being the preferred analytical method. The standardization of a robust ion chromatographic method is therefore of the high interest for public health and safety. This article summarizes the experiments and results obtained from analyzing untreated samples, considering the sample’s electrical conductance as guidance for direct sample injection as described in EPA 314.0. The suitability of ion chromatography with suppressed conductivity detection was tested for water samples in order to check the influence of matrix effects on the perchlorate signal of untreated samples. A sample injection volume of 750 μL was applied to the selected 2 mm IC column. The IC determination of perchlorate at low µg/L levels is challenged by the presence of high loads of matrix ions (e.g., chloride, nitrate, carbonate, and sulfate at 100 mg/L and above). Perchlorate recovery is impaired with the increasing matrix ion concentrations, and its chromatographic peak is asymmetric particularly at low perchlorate concentrations. The identification of the individual maximum concentration of interfering anions like chloride, nitrate, and sulfate that influence perchlorate recovery helps to reduce the number of sample preparation steps or an obligatory measurement of the electrical conductivity of the sample. Within the scope of this study, samples containing less than 125 mg/L of either anion did not need sample preparation. The identification of the maximum concentration of interfering anions like chloride, nitrate, and sulfate influencing perchlorate recovery provides a simplified alternative to the EPA 314.0 method. This approach reduces unnecessary sample preparation steps while allowing a reliable prognosis of possible interferences and maintaining result quality. This study was performed to support the development of a respective international standard, which is being established by the International Organization for Standardization (ISO). The results of the study are also intended to be used as guidance for interested laboratories to optimize the analytical workflow for trace perchlorate determination.

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.

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)0.001
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 score0.324
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.027
GPT teacher head0.296
Teacher spread0.269 · 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