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Record W2596298946 · doi:10.1088/0026-1394/54/1a/08010

Contaminant and other elements in soil (CCQM-K127)

2017· article· en· W2596298946 on OpenAlexaff
M Rocio Arvizu Torres, Judith Velina Lara Manzano, Edith Valle Moya, Milena Horvat, Radojko Jačimović, Tea Zuliani, Polona Vreča, Osvaldo Reyes Acosta, John Bennet, James Snell, Marcelo Dominguez de Almeida, Rodrigo Caciano de Sena, Emily Silva Dutra, Lu Yang, Haifeng Li, Jingbo Chao, Paola Fisicaro, Michael H P Yau, Wai-hong Fung, Shankar G. Aggarwal, Daya Soni, Shin‐ichi Miyashita, Christian Uribe, Mirella Buzoianu, L A Konopelko, Linsky, Oktay Cankur, Süleyman Z. Can, Michael R. Winchester, Savelas A. Rabb, Karen E. Murphy, George C. Caceres, Tom Oduor Okumu

Bibliographic record

VenueMetrologia · 2017
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsEnvironmental scienceSoil waterSoil contaminationEnvironmental chemistrySmeltingContaminationSoil testEnvironmental remediationSoil scienceChemistry

Abstract

fetched live from OpenAlex

Non-contaminated soils contain trace and major elements at levels representing geochemical background of the region. The main sources of elements as contaminants/pollutants in soils are mining and smelting activities, fossil fuel combustion, agricultural practices, industrial activities and waste disposal. Contaminated/polluted sites are of great concern and represent serious environmental, health and economic problems. Characterization and identification of contaminated land is the first step in risk assessment and remediation activities. It is well known that soil is a complex matrix with huge variation locally and worldwide. According to the IAWG's five year plan, it is recommended to have a key comparison under the measurement service category of soils and sediments for the year 2015. Currently 13 NMI has claimed calibration and measurement capabilities (CMCs) in category 13 (sediments, soils, ores, and particulates): 29 CMCs in soil and 96 CMCs in sediments. In this regard this is a follow-up comparison in the category 13; wherein three key comparisons have been carried out during the years 2000 (CCQM-K13), 2003 (CCQM-K28) and 2004 (CCQM-K44). Since it is important to update the capabilities of NMIs in this category. CENAM and JSI proposed a key comparison in this category and a pilot study in parallel. The proposed study was agreed by IAWG members, where two soils samples were used in both CCQM-K127 representing a non-contaminated soil with low contents of elements (arsenic, cadmium, iron, lead and manganese), and a contaminated soil with much higher content of selected elements (arsenic, cadmium, iron and lead). This broadens the scope and a degree of complexity of earlier measurements in this field. National metrology institutes (NMIs)/designate institutes (DIs) should, therefore, demonstrate their measurement capabilities of trace and major elements in a wide concentration ranges, representing background/reference sites as well as highly contaminated soils by their available analytical methods. This facilitated the investigation into the core capabilities of participants to measure the mass fraction of tested elements in soil and therefore to claim their CMCs as listed in appendix C of the key comparison database (KCDB) under the mutual recognition arrangement of the International Committee for Weights and Measures (CIPM MRA). In total 19 institutes (NMIs/Dis) participated in the key comparison and the reported results of the key comparison were from 18 institutes (NMIs/DIs); 151 measurements were reported for CCQM-K127. The analytical techniques selected by the participant institutes were ICP-MS, ICP-OES, FAAS, ET-AAS and INAA ( k 0 -method of INAA); the sample preparation methods used were based on microwave assisted digestion, except when it was used INAA. After discussions it agreed to use the median as KCRV and the MMADe as u ( x KCRV ). Generally most of the results of the participants were found to be consistent for all measurements according to their equivalence statements, with the exception of some extreme values, which were identified with a value of d i /U(d i ) higher than 1. This key comparison is a means of providing evidence for practical demonstration of a CCQM comparison calibration and measurement capabilities (CMCs) claims for contaminant and others elements, in low and medium content levels in non-contaminated and contaminated matrices described in category 13. Main text 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 kcdb.bipm.org/ . 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.272

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.258
Teacher spread0.234 · 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 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

Citations3
Published2017
Admission routes1
Has abstractyes

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