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Record W2793515482 · doi:10.1107/s1600577518000954

A Li<i>K</i>-edge XANES study of salts and minerals

2018· article· en· W2793515482 on OpenAlexafffund
Cedrick O’Shaughnessy, Grant S. Henderson, Benjamin J.A. Moulton, Lucia Zuin, Daniel R. Neuville

Bibliographic record

VenueJournal of Synchrotron Radiation · 2018
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsCanadian Light Source (Canada)University of SaskatchewanUniversity of Toronto
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsXANESElectronegativityAluminosilicateLithium (medication)K-edgeChemistryAbsorption (acoustics)Absorption edgeBond lengthAnalytical Chemistry (journal)Absorption spectroscopyInorganic chemistryCrystallographyMaterials scienceSpectral lineBand gapPhysicsCrystal structureOptics

Abstract

fetched live from OpenAlex

The first comprehensive Li K -edge XANES study of a varied suite of Li-bearing minerals is presented. Drastic changes in the bonding environment for lithium are demonstrated and this can be monitored using the position and intensity of the main Li K -absorption edge. The complex silicates confirm the assignment of the absorption edge to be a convolution of triply degenerate p -like states as previously proposed for simple lithium compounds. The Li K -edge position depends on the electronegativity of the element to which it is bound. The intensity of the first peak varies depending on the existence of a 2 p electron and can be used to evaluate the degree of ionicity of the bond. The presence of a 2 p electron results in a weak first-peak intensity. The maximum intensity of the absorption edge shifts to lower energy with increasing SiO 2 content for the lithium aluminosilicate minerals. The bond length distortion of the lithium aluminosilicates decreases with increasing SiO 2 content, thus increased distortion leads to an increase in edge energy which measures lithium's electron affinity.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.338

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 science0.0000.000
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 head0.010
GPT teacher head0.291
Teacher spread0.280 · 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 designBench or experimental
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

Citations17
Published2018
Admission routes2
Has abstractyes

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