Tribute to Derek Long: An instant snapshot of the development of Raman spectroscopy and its application in the fields of instrumentation and methodology, solid‐state materials, cultural heritage, DFT modeling and applications in biology, microbiology, and medicine
Bibliographic record
Abstract
This special issue of the Journal of Raman Spectroscopy dedicated to ProfessorDerek Long provides a snapshot of the uses and improvements made in the technique, the development of which he was personally deeply involved in both fundamentally, theoretically and in the field of its applications.Data collected by the web of science indicate 75 articles for its activity at the University of Bradford from 1975 to 2008 (for a total of about 200), [1] listed under the qualifying categories: spectroscopy (82%), physical (66%), chemistry (32%), crystallography (21%), biophysics (8%), engineering (8%), cell biology (7%), instrumentation (4%), optics (3%), and polymer science (3%).However, there is no doubt that his main impact on the community of Raman spectroscopists is certainly his two textbooks, the first entitled "Raman Spectroscopy" published in 1977 by McGraw-Hill (270 pp) [2] and the second "The Raman Effect: A Unified Treatment of the Theory of Raman Scattering by Molecules" published in 2002 by John Wiley [3] (584 pp) and also the foundation with H.J. Bernstein (NRCC, Ottawa, Canada) as Editors-in-Chief along with J.P. Mathieu (
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.035 | 0.029 |
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".