Bibliographic record
Abstract
The smart specialization declared in the European program Horizon 2020, and the increasing cooperation between research and development found in companies and researchers at universities and research institutions have created a new paradigm where many calls for proposals require participation and funding from public and private entities. This has created a unique opportunity for large-scale facilities, such as synchrotron research laboratories, to participate in and support applied research programs. Scientific staff at synchrotron facilities have developed many advanced tools that make optimal use of the characteristics of the light generated by the storage ring. These tools have been exceptionally valuable for materials characterization including X-ray absorption spectroscopy, diffraction, tomography and scattering, and have been key in solving many research and development issues. Progress in optics and detectors, as well as a large effort put into the improvement of data analysis codes, have resulted in the development of reliable and reproducible procedures for materials characterization. Research with photons has contributed to the development of a wide variety of products such as plastics, cosmetics, chemicals, building materials, packaging materials and pharma. In this review, a few examples are highlighted of successful cooperation leading to solutions of a variety of industrial technological problems which have been exploited by industry including lessons learned from the Science Link project, supported by the European Commission, as a new approach to increase the number of commercial users at large-scale research infrastructures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".