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Improvements in Digestion Protocols for Trace Element and Isotope Determinations in Stream and Lake Sediment Reference Materials (JSd‐1, JSd‐2, JSd‐3, JLk‐1 and LKSD‐1)

2009· article· en· W2108718792 on OpenAlexaboutno aff
Sidonie Révillon, D. Hureau-Mazaudier

Bibliographic record

VenueGeostandards and Geoanalytical Research · 2009
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
FundersCentre National de la Recherche ScientifiqueInstitut Français de Recherche pour l'Exploitation de la Mer
KeywordsDissolutionCertified reference materialsTrace elementSedimentEnvironmental chemistryIsotopeZirconChemistryEnvironmental scienceMineralogyGeologyChromatographyGeochemistryDetection limitPhysics

Abstract

fetched live from OpenAlex

Total dissolution is a critical step in geochemical analysis. Despite the number of published protocols, this issue still draws attention for sediment samples, which are particularly difficult to dissolve due to the common occurrence and high abundance of refractory phases such as zircon. We present tests of different chemical digestion procedures carried out on reference materials (RM) of stream (JSd‐1, JSd‐2 and JSd‐3) and lake (JLk‐1 and LKSD‐1) sediments from the Geological Survey of Japan (GSJ) and the Canadian Certified Reference Material Programme (CCRPM). We demonstrate that the fusion technique is not appropriate for our studies as not all elements of interest were recovered and blank levels were too high to permit further Sr and Pb isotopic composition measurements. Similarly, conventional HF+HNO 3 dissolution methods were not efficient enough for detrital samples. Our preferred method involved using high pressure Teflon® vessel bombs in association with HClO 4 . This protocol ensured a complete dissolution of the powder, as well as a complete recovery of trace elements. Moreover, blank levels were sufficiently low that Sr or Pb isotope compositions could be measured from the same mother solution. We also tested the homogeneity of RM powders by performing tests on various amount of powder.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.059
GPT teacher head0.394
Teacher spread0.335 · 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 designOther design
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

Citations34
Published2009
Admission routes1
Has abstractyes

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