Practical approaches to the ESI‐MS analysis of catalytic reactions
Why this work is in the frame
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Bibliographic record
Abstract
Electrospray ionization mass spectrometry (ESI-MS) is a soft ionization technique commonly coupled with liquid or gas chromatography for the identification of compounds in a one-time view of a mixture (for example, the resulting mixture generated by a synthesis). Over the past decade, Scott McIndoe and his research group at the University of Victoria have developed various methodologies to enhance the ability of ESI-MS to continuously monitor catalytic reactions as they proceed. The power, sensitivity and large dynamic range of ESI-MS have allowed for the refinement of several homogenous catalytic mechanisms and could potentially be applied to a wide range of reactions (catalytic or otherwise) for the determination of their mechanistic pathways. In this special feature article, some of the key challenges encountered and the adaptations employed to counter them are briefly reviewed.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it