Bibliographic record
Abstract
Good afternoon. I am David Wong, President-elect of the American Association for Dental Research, and it’s my pleasure to welcome you to the 2010 joint Annual Meetings of the AADR and the Canadian Association for Dental Research. I would like to extend a special welcome to our new members and the students who are joining us this year. I hope that each of you finds the next few days informative and stimulating. There has never been a more important time for health care professionals to gather to discuss what we do and how we do it . . . because there has never been a time when health care has been so much in the forefront of our minds as individuals and as a nation. It is critical for us to ensure that we, as oral health specialists, are part of that conversation, because our research is increasingly being recognized as a key to unlocking not just oral and craniofacial diseases, but systemic issues as well. It is now abundantly clear that while oral and craniofacial abnormalities make up one piece of the body’s complex puzzle, they are, in many ways, a mirror of the entire system. Just as we have learned that each part of the body has an effect on the entire system, we are also realizing that there is no one-size-fits-all way to diagnose and treat diseases. Both in our own field of dentistry and in other disciplines, individualized medicine is certain to be one of the most important concepts in health care research in the coming years. Dr. Francis Collins, the new Director of NIH, authored a Policy Forum article in the January issue of Science for “Opportunities for Research and NIH”, where he detailed five areas of opportunity he sees as holding particular promise for NIH: high-throughput technologies, translational medicine, benefiting health care reform, focusing more on global health, and reinvigorating and empowering the biomedical research community. These are the opportunities we need to seize to advance oral health research, dentistry and global health. Thank you all for being here. I hope that each of you will come away from this week with new information about the breakthroughs our colleagues are making . . . and a renewed dedication to the important contributions we are making to the health of our nation and the world.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.355 | 0.402 |
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".