Bibliographic record
Abstract
The SRNT Trainee Network Spotlight highlights outstanding trainees in tobacco science, thereby providing visibility and networking opportunities. Please visit the Trainee Network’s website (www.srnt.org/mem_only/networks/trainee.cfm) to learn more about trainee webinars, the trainee mixer event at the annual meeting, and joining the network. ... Dr Mead completed her PhD in Public Health at the Bloomberg School of Public Health at Johns Hopkins University in 2014. She is currently a Post-Doctoral Fellow at the University of Maryland, Tobacco Center of Regulatory Science. Dr Mead’s recent accomplishments in tobacco science include publishing a study on the role of novel, theory-driven graphic warning labels in motivation to quit among low-income, urban smokers and receiving a distinguished Doctoral Research Award from the Bloomberg School of Public Health. Dr Mead became interested in tobacco science after observing significant tobacco-related disparities in a vulnerable, underserved population while working with small native communities in the Canadian Arctic. Her favorite parts of being an SRNT member include exposure to innovative research, exchange of ideas, and opportunities to participate in cutting edge workshops. Dr Mead’s future training goals include training in transdisciplinary tobacco regulatory science, developing expertise in the use of mobile health technology for research and interventions, and continuing work in tobacco disparities research using a culturally competent approach.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.694 | 0.439 |
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".