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Record W2163288382 · doi:10.1139/cjc-2014-0587

Comment on “Spectroscopic studies of oxygen speciation in potassium silicate glasses and melts”

2015· article· en· W2163288382 on OpenAlexvenueno aff
Wim J. Malfait

Bibliographic record

VenueCanadian Journal of Chemistry · 2015
Typearticle
Languageen
FieldMaterials Science
TopicGlass properties and applications
Canadian institutionsnot available
Fundersnot available
KeywordsX-ray photoelectron spectroscopyChemistrySilicateNMR spectra databaseSpectral lineAnalytical Chemistry (journal)OxideOxygenElectron microprobeMineralogyNuclear magnetic resonancePhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Recent O1s XPS studies suggest that significant free oxide (O 2– or K–O–K) is present in potassium silicate glasses, in contrast with what was concluded from 29 Si and 17 O solid-state NMR data. An alternative peak assignment of the Q n peaks in the 29 Si NMR spectra was proposed to bring the 29 Si NMR data in line with the O1s data, but this reassignment (i) is not supported by any evidence other than the resulting agreement between the NMR and XPS data, (ii) is at odds with the spectral properties of the bands (peak position and width), (iii) ignores the strong evidence for the original peak assignment, and (iv) results in highly implausible equilibrium constants for the Q n speciation reactions. More likely causes for the apparent discrepancy between the XPS and NMR data are the incorrect estimation of the precision of the bridging oxygen content from the O1s XPS data, the systematic overestimation of the bridging oxygen content from the O1s XPS data, and (or) the incorrect estimation of the accuracy of the compositional analysis, which was based on the precision rather than the accuracy of the electron microprobe analysis. Thus, the available evidence strongly suggests that the original assignment of the 29 Si NMR spectra is correct. Neither the 29 Si NMR nor the O1s XPS data support the presence of significant amounts of free oxide in potassium silicate glasses with K 2 O/SiO 2 ≪ 2.

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 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 score0.010
Threshold uncertainty score0.191

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)0.0000.000

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 head0.043
GPT teacher head0.269
Teacher spread0.227 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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