CANADIAN: Shining a light on “NMR-invisible” biocidal borate glasses
Bibliographic record
Abstract
Dissolvable copper-sodium-borate glass has found an industrial application as a wood preservative, where the biocidal copper and borate ions in solution slowly infiltrate and bind to wood’s chemical components, leading to an inhospitable environment for most microorganisms, tripling lifetimes of wood products (approx. ten years). The unique dissolution behaviour, which is paramount to the successful application of these glasses, is dependent on the copper-borate bonding in the glassy state: one cannot simply add the starting reagents to water and expect the same infiltration behaviour. The main goal is to understand the chemical bonding within the glass that results in the strong infiltration of the aqueous copper-borate complexes into the wood. Discernment of the different bonding environments in the glass is more reliable than in the solution state, and would aid future investigations of aqueous coordination complexes, likely by X-ray absorption. We will extract accurate information about the boron and copper bonding environments from neutron total scattering and x-ray total scattering, respectively (including dependence on independently measured oxidation state). In addition to fundamental understanding about copper in borates glass, we wish to quantitatively relate molecular structure to the macroscopic properties, such as dissolution behaviour, physical and mechanical properties.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.032 |
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".