Following the initial success of the Asian Society of Gyneco- logic Oncology (ASGO) 1st Biennial Meeting Tokyo in 2009,
Bibliographic record
Abstract
a resounding success. It saw active participation from 15 Asian countries as well as 5 non Asian countries (USA, UK, Canada, Germany, and Eritrea). The total number of registrants was 622 with a total of 226 abstract submissions (Table 1). The theme for this year meeting was ‘New Insight into Gy-necologic Cancer in Asia ’ and the purpose was to serve as a platform for mutual exchange and sharing of the latest de-velopments in the field of gynecologic oncology in Asia. The scientific program was very comprehensive (Fig. 1). It covers all aspects of gynecologic oncology. There were 5 plenary sessions, covering various pertinent management issues and updates in uterine, cervical, ovarian cancers and gestational trophoblastic diseases as well as a special plenary session on controversial issues in gynecologic oncology and one entire plenary session focusing on translational research. There were 3 free communication sessions and poster display with a total of 29 oral and 133 poster presentations. ASGO was also hon-ored to have 4 renowned speakers from International Gyneco-
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".