Recent advances in linear and non‐linear Raman spectroscopy. Part XI
Bibliographic record
Abstract
Abstract This review, published annually, provides an overview of advances in the field of Raman spectroscopy as found in papers published in the preceding calendar year in the Journal of Raman Spectroscopy (JRS), as well as in trends over the past decade across journals that have published papers important to the field of Raman spectroscopy. This information is obtained from statistical data on article counts obtained from Clarivate Analytics' Web of Science Core Collection by year and by subfield of Raman spectroscopy. Additional information is gleaned from presentations at the IX International Conference on Advanced Vibrational Spectroscopy (ICAVS‐9) in Victoria, British Columbia, Canada, in June 2017 and those featuring Raman scattering at SCIX 2017 organized by the Federation of Analytical Chemistry and Spectroscopy Societies (FACSS) in Reno, Nevada, USA, in October 2017. Coverage is also provided for topics from the European Conference on Non‐linear Optical Spectroscopy (ECONOS 2017) held in April 2017 in Jena, Germany, and the Ninth Congress on the Application of Raman in Art and Archaeology (RAA 2017) in October 2017 in Evora, Portugal. Finally, papers published in JRS in 2016 are highlighted and arranged by topics at the frontier of Raman spectroscopy. Based on this survey information, it is clear that Raman spectroscopy maintains a rapidly expanding influence across a wide range of novel disciplines and applications that provide sensitive photonic information of matter at the molecular level.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".