Bibliographic record
Abstract
This grant was used to provide participant support for a symposium entitled “Chemistry of Atmospheric Aerosols” at the 2015 International Chemical Congress of Pacific Basin Societies (Pacifichem) that took place in Honolulu, Hawaii, USA, on December 15-20, 2015. The objective was to help attract both distinguished scientists as well as more junior researchers, including graduate students, to this international symposium by reducing the financial barrier for its attendance. It was the second time a symposium devoted to Atmospheric Aerosols was part of the Pacifichem program. This symposium provided a unique opportunity for the scientists from different countries to gather in one place and discuss the cutting edge advances in the cross-disciplinary areas of aerosol research. To achieve the highest possible impact, the PI and the symposium co-organizers actively advertised the symposium by e-mail and by announcements at other conferences. A number of people responded, and the end result was a very busy program with about 100 oral and poster presentation described in the attached PDF file. Presentations by invited speakers occupied approximately 30% of time in each of the sessions. In addition to the invited speakers, each session also had contributed presentations, including those by graduate students and postdoctoral researchers. This symposium gathered established aerosol chemists from a number of countries including United States, Canada, China, Japan, Korea, Australia, Brazil, Hongkong, Switzerland, France, and Germany. There were plenty of time for the attendees to discuss new ideas and potential collaborations both during the oral sessions and at the poster sessions of the symposium. The symposium was very beneficial to graduate student researchers, postdoctoral fellows, and junior researchers whose prior exposure to international aerosol chemistry science had been limited. The symposium provided junior researchers with a much broader perspective of aerosol chemistry than that afforded by attending a national meeting. The oral and platform presentation abstracts from the symposium were published in the Pacifichem Congress program.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.017 |
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".