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Record W3040300966 · doi:10.1111/ggr.12351

Potassium and Calcium <i>K</i>‐Edge XANES in Chemical Compounds and Minerals: Implications for Geological Phase Identification

2020· article· en· W3040300966 on OpenAlexaff
Wenshuai Li, Xiaoming Liu, Yongfeng Hu

Bibliographic record

VenueGeostandards and Geoanalytical Research · 2020
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersNational Science Foundation
KeywordsXANESIgneous rockSilicateGeologyMineralogyPotassiumPhase (matter)Silicate mineralsChemistrySpectroscopyGeochemistryPhysics

Abstract

fetched live from OpenAlex

Potassium (K) and calcium (Ca) K ‐edge X‐ray adsorption near‐edge (XANES) spectroscopy were performed on thirty‐three chemical compounds and geological materials, including chemical reagents, organometallic compounds, silicates, carbonates and igneous rock reference materials. The results confirm that the fine structure of the K ‐edges for specimens is unique and distinguishable. The results suggest that compositional and local atomic variations strongly regulate spectral characteristics. Acquired XANES spectra with the library of distinctive spectral features of model references approve the fingerprint identification of different phases of K and Ca involved in geological materials. Moreover, this reveals that typical compositional changes in geological samples could strongly affect spectral features. As an example, we quantitatively determined the silicate species of K and Ca in two igneous rock reference materials by linear combination fitting. The dominant hosts and molecular environments of K and Ca can be interpreted based on pre‐edge/post‐edge peak position, intensity, shifts and resonance features, thus improving the understanding of the (bio)geochemical cycling, partitioning and isotopic fractionation of K and Ca. The outcomes serve as a complementary database for a vast number of scientific contexts, including aspects of geological and environment sciences.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 science0.0000.001
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 head0.133
GPT teacher head0.410
Teacher spread0.277 · 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 designTheoretical or conceptual
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

Citations29
Published2020
Admission routes1
Has abstractyes

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