Highlights of the 12th International<i>Bordetella</i>Symposium
Bibliographic record
Abstract
To commemorate the 100th anniversary of the Nobel prize being awarded to Jules Bordet, the discoverer of Bordetella pertussis, the 12th International Bordetella Symposium was held from 9 to 12 April 2019 at the Université Libre de Bruxelles, where Jules Bordet studied and was Professor of Microbiology. The symposium attracted more than 300 Bordetella experts from 34 countries. They discussed the latest epidemiologic data and clinical aspects of pertussis, Bordetella biology and pathogenesis, immunology and vaccine development, and genomics and evolution. Advanced technological and methodological tools provided novel insights into the genomic diversity of Bordetella and a better understanding of pertussis disease and vaccine performance. New molecular approaches revealed previously unrecognized complexity of virulence gene regulation. Innovative insights into the immune responses to infection by Bordetella resulted in the development of new vaccine candidates. Such discoveries will aid in the design of more effective approaches to control pertussis and other Bordetella-related diseases.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 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".