Ripmeester, John A.: Forty-Plus Years of Research in Solid-State NMR Spectroscopy
Bibliographic record
Abstract
As in the case of most researchers, I spent the early part of my career in learning the fundamentals—in this case, solid-state NMR as practiced in the late 1960s and early 1970s. This background eventually gave me employment at the National Research Council of Canada where my expertise was seen as a valuable asset to venture into detailed studies of dynamics in clathrate hydrates. This led to the exploration of new approaches, e.g., the exploration of 129Xe NMR spectroscopy for the characterization of pore space as well as the exploration of new materials—generally classifiable as guest–host and supramolecular solids. The research group which I led for a number of years, with expanded expertise in NMR and complementary techniques, became known for the ability to characterize complex materials where disorder and dynamics were a problem. With the recent global interest in natural gas hydrates, my interests have swung back to concentrating on understanding structures and processes in this challenging class of labile minerals.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 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".