Bibliographic record
Abstract
ORLANDO—Attendance at the 95th Annual Meeting of the American Association for Cancer Research was better than for any year in recent memory, except for 2002 in San Francisco, organizers said. Just under 14,000 people—13,948, to be exact—were registered to hear or view the approximately 5,700 presentations. In contrast, the 2002 meeting drew 15,403 attendees, and there were 12,205 people pre-registered for the 2003 Toronto meeting that was aborted because of the SARS scare. While that cancellation meant that 2003–2004 President Karen Antman, MD, had only nine months in office, it didn't interfere with planning for the Orlando meeting, she said. “We had our first program committee meeting even before the Toronto meeting was canceled.” But sandwiching two conferences into nine months—the replacement meeting in Washington in July and then this year's in Orlando—meant considerable extra work for the AACR staff.Figure: Scenes from the Annual Meeting: (clockwise from top) AACR booth in the Exhibit area; AACR CEO Margaret Foti, PhD; President Lynn M. Matrisian, PhD (left) and 2003–04 President Karen Antman, MD; and Gregory M. Wahl, PhD, Program Chair.“It was really tougher for the staff than it was for us [the elected officers and committee chairs],” Dr. Antman said. The theme of this year's meeting was Information Integration for Innovation, and that's what registrants felt took place, said Program Chair Geoffrey M. Wahl, PhD, Professor of Genetics at the Salk Institute for Biological Studies. “People have told me how excited they are because we tried to do integration of information. I think it has been a fabulous conference, but I'm biased.” 2004-2005 President Lynn McCormick Matrisian, PhD, of Vanderbilt University, said the conference showcased “a lot of cutting-edge science, as well as discussion of the way the future should go.” A challenge for the 2005 annual meeting (April 16 to 20 in Anaheim, CA) and for the Association in general, will be to involve the public more in the discussions, she said. “We need to find out how to translate our information into the type of information that the public is interested in. We did have a very successful Public Forum [held this year as it has for the last several years, on the first day of the meeting], but it's such a small percentage of the population that we reach. We still have a long way to go.”
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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 teacher head, 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".