Preparing the new generation of Romanian experts in tobacco control: PhD studies in the field
Bibliographic record
Abstract
Scientific progresses and new regulatory global context about tobacco use and cessation have created attractive opportunities for young researchers seeking for a PhD in the field. As such, three most relevant ongoing PhD research topics at the University of Medicine and Pharmacy “Grigore T. Popa” Iasi were selected for presentation: 1. “Air pollution and tobacco smoke interactions in COPD” (to investigate cumulated effect of tobacco smoking and air pollution on clinical course and treatment benefits in COPD, in the actual upsetting increased local environmental pollution frame). 2. “Smoking and oxidative stress in mixed anxious and depressive disorder”( to reveal interactions between oxidative stress and tobacco smoking in mixed anxious an depressive disorders, to ascertain clinical practice benefits of tobacco exposure biomarkers in use and propose new such markers 1 to improve disease management). 3. “Personalized tobacco treatment" algorithms for smokers at high respiratory risk” (to develop and implement clinical practice personalized algorithms to treat tobacco dependence in smokers at high risk for respiratory disease and to test their feasibility in a real life respiratory disease clinical setting).
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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".