Bibliographic record
Abstract
AIRAPT-27th International Conference on High-Pressure Science and Technology Preface The AIRAPT-27 International Conference on High-Pressure Science and Technology was held in Rio de Janeiro, Brazil, from August 4th to 9th, 2019. This was the first time the event occurred in the South Hemisphere. The scientific program consisted of 27 oral and 2 poster sections, and 8 plenary sections, including the Bridgman and Jamieson Awards. In the event, 40 Invited Lectures, 95 oral contributions and 58 posters were presented. The topics covered 17 areas dealing with fundamental and applied research in the field of High-Pressure Science and Technology. 193 scientists and engineers, including students, attended the conference (164 men and 29 women), coming from 20 countries: Argentina (1), Brazil (47), Canada (2), China (21), France (13), Germany (14), Hungary (1), Israel (4), Italy (3), Japan (15), Poland (4), Qatar (1), Russia (10), Slovakia (1), South Africa (1), South Korea (1), Spain (5), Sweden (2), United Kingdom (10), and United States of America (37). List of Chairpersons, Organizing Committee, International Advisory Committee, Program Committee, Conference Proceedings Committee, Host Institutions (Brazil), Supported by (Brazilian agencies) and Sponsorships are available in this pdf.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.529 | 0.391 |
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".