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Record W4403249337 · doi:10.1088/0026-1394/61/1a/08016

CCQM-K161 "Anions in Seawater"

2024· article· en· W4403249337 on OpenAlexaff
Jingbo Chao, Ma Liandi, Naijie Shi, Li Yunqiao, Yan Chen, Dong Lijie, Zhou Yuanjing, Patrícia Grinberg, Zoltán Mester, Enea Pagliano, Henry Torres Quezada, Johanna Paola Abella Gamba, Ibrahim F. Tahoun, Olaf Rienitz, Jessica Towara, Carola Pape, Ursula Schulz, Anita Roethke, Wai-hong Fung, Jasmine Po-kwan Lau, Queenie Kwok-wai Chan, Kelvin Chun-wai Tse, Chikako Cheong, V. I. Dobrovolskiy, S. V. Prokunin, D A Vengina, А. В. Собина, Alexandr Shimolin, R.Y.C. Shin, Wesley Zongrong Yu, H.W. Leung, Nongluck Tangpaisarnkul, Patumporn Rodruangthum, Süleyman Z. Can, F Gonca Coskun, Oktay Cankur

Bibliographic record

VenueMetrologia · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicChemical Analysis and Environmental Impact
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSeawaterEnvironmental scienceOceanographyGeology

Abstract

fetched live from OpenAlex

Main text Anions or nutrients in seawater are very important targets for oceanographic research and environmental monitoring of contaminations. Quantification of minor and trace anions in seawater has always been a challenge for the extremely high salinity, disparate levels of analyte and matrix ions, and even need to be measured at levels close to the detection limits of the method performance. Evidence of successful participation in formal, relevant international comparisons is needed to support calibration and measurement capability claims (CMCs) made by the national metrology institutes (NMIs) and designated institutes (DIs). The CCQM-K161 Anions in Seawater was organized by the Inorganic Analysis Working Group (IAWG) of the Consultative Committee for Amount of Substance: Metrology in Chemistry and Biology (CCQM) to assess the abilities of the NMIs and DIs for the accurate determination of minor and trace anions in seawater. The measurands covered chloride (16 mg/g-25 mg/g), sulfate (1 mg/g-4 mg/g), bromide (30 mg/kg-100 mg/kg), nitrate (1 mg/kg-5 mg/kg) and phosphate (60 µg/kg-300 µg/kg). Twelve national metrology institutes and designated institutes participated in this key comparison. Participants were requested to evaluate the mass fractions, expressed in mg/g for chloride and sulfate, mg/kg for bromide and nitrate, and µg/kg for phosphate (as phosphorus) in a mixed natural seawater that was spiked with the phosphate. A variety of techniques including isotope dilution inductively coupled plasma mass spectrometry (IDMS), isotope dilution gas chromatography-mass spectrometry (ID-GC-MS), ion chromatography (IC), UV visible spectrophotometry (UV-Vis), flow injection analysis (FIA) was used by the participants for the determination. The NIST Decision Tree was used to assign the KCRV estimate and to calculate the degrees of equivalence of each participants following the IAWG Guidance on Using NIST Decision Tree for Comparison Reporting from 30 June 2023. Successful participation in CCQM-K161 demonstrates measurement capabilities for determination of anions in seawater. Considering the IAWG Core Capability Matrix, this material falls into the matrix challenge called 'High salts content', which corresponds to the CCQM amount-of-substance category sea water, and so will support CMCs for the anions in a mass fraction range from 60 µg/kg to 25 mg/g. To reach the main text of this paper, click on Final Report . Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/ . The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.997

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)0.0110.004

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 head0.005
GPT teacher head0.225
Teacher spread0.219 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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