Scientists on the Spot: Myocardium and myofilaments
Bibliographic record
Abstract
Watch the interview here:https://youtu.be/nz4riOkoKU8 Highlight: In this Onlife interview, Professor de Tombe discusses his research into the mechanisms of sarcomere length in heart muscle, how this relates to heart failure and future treatment avenues, as well as his advice for young scientists on being open to disproving your hypotheses. Biography: Prof. Pieter de Tombe is Professor Emeritus of Physiology at the University of Illinois in Chicago. He is known worldwide for his excellent expertise in cardiac muscle physiology. He received training in Chemistry in the Netherlands and then received his PhD in Physiology at the University of Calgary in Canada in 1989. He worked as a postdoc in bioengineering at the Johns Hopkins University (MD, USA) from 1989 to 1991. He became an Assistant Professor at Wake-Forest University (NC, USA) in 1991 and then Associate Professor in 1996. He joined the Department of Physiology and Biophysics at the University of Illinois at Chicago (IL, USA) where he was appointed Full Professor of Physiology in 2002. He chaired the department of Cell and Molecular Physiology at the Loyola University Chicago Stritch School of Medicine. Prof. De Tombe is Visiting Professor at the University of Freiburg (Germany) and PhyMedExp laboratory in Montpellier (Inserm U1046, France).
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.119 | 0.036 |
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".