Abstracts
Bibliographic record
Abstract
I am pleased to invite you to attend the 61st Annual Meeting of the Society for Birth Defects Research and Prevention, a fully online, virtual experience that will take place weekdays from June 24 to July 1, 2021, with three Education Courses scheduled later in July.We believe this meeting format is the safest approach for the well-being of our members and speakers, and we are excited that we can build on the successes and lessons learned through our impactful 2020 virtual meeting.The Program Committee, chaired by Susan Makris, has put together an outstanding program around the meeting theme which is "Building Bridges in Birth Defects Research and Prevention: From Innovation to Application."The Keynote Lecture will be given by Carleigh Krubiner, Johns Hopkins Berman Institute of Bioethics.Dr. Krubiner's research focuses on ethical issues surrounding the equitable development and delivery of health interventions in low and middle-income settings.For the past several years, Dr. Krubiner has co-led work focusing on how epidemic vaccine development and deployment can be more responsive to the health interests of pregnant women, with more recent work focusing on COVID-19 vaccine R&D and distribution.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.671 | 0.518 |
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".