Interview with the ECM Award winner 2022 and introducing the new ECM members
Bibliographic record
Abstract
The Early Career Member (ECM) Award is intended to honour promising members of the European Respiratory Society (ERS) at an early stage of their professional career, based on their curriculum vitae, involvement in the ERS and potential for future scientific contributions. This award is given during the ERS International Congress, where the ECM Awardee is invited to give the Mina Gaga lecture during the ECM session. In this article, we present an interview conducted with the 2022 ECM Award winner, Alexander Mathioudakis, where he discussed his work and visions for the future and shared some tips for ECMs starting a career in respiratory research. We also provide a brief introduction to the new members of the ECM Committee (ECMC) from Assemblies 2 (Respiratory intensive care), 3 (Basic and translational sciences), 7 (Paediatrics) and 8 (Thoracic surgery and transplantation). This article presents the interview with the ERS Early Career Member Awardee 2022 (@MathioudakisAG) and provides a brief introduction to the new ECM members The authors would like to acknowledge Alexander Mathioudakis for his collaboration on the interview and for reviewing the transcript, and to Anush Meliksetyan and Olivia Menegale (European Respiratory Society, Lausanne, Switzerland) for their support in scheduling and recording the interview.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".