Comment on “Spectroscopic studies of oxygen speciation in potassium silicate glasses and melts”
Bibliographic record
Abstract
Recent O1s XPS studies suggest that significant free oxide (O2– or K–O–K) is present in potassium silicate glasses, in contrast with what was concluded from 29Si and 17O solid-state NMR data. An alternative peak assignment of the Qn peaks in the 29Si NMR spectra was proposed to bring the 29Si 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 Qn 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 29Si NMR spectra is correct. Neither the 29Si NMR nor the O1s XPS data support the presence of significant amounts of free oxide in potassium silicate glasses with K2O/SiO2 ≪ 2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.041 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".