Bibliographic record
Abstract
Release for Zenodo archiving of the code related to mathematical modeling and parameter estimation in the paper Taisuke Kondo=, François X. P. Bourassa=, Sooraj R. Achar=, J. DuSold, P. F. Céspedes, M. Ando, A. Dwivedi, J. Moraly, C. Chien, S. Majdoul, A. L. Kenet, M. Wahlsten, A. Kvalvaag, E. Jenkins, S. P. Kim, C. M. Ade, Z. Yu, G. Gaud, M. Davila, P. Love, J. C. Yang, M. Dustin, Grégoire Altan-Bonnet, Paul François, and Naomi Taylor."Engineering TCR-controlled Fuzzy Logic into CAR T-Cells Enhances Therapeutic Specificity", Cell [accepted in principle], 2025. (=: these authors contributed equally) We developed mathematical models to quantitatively predict TCR and CAR cross-receptor interactions in CAR T cells and compare to experimental data. The necessary datasets and model results files are hosted on Zenodo: Zenodo repository link See more instructions on the Github repository: https://github.com/frbourassa/tcr_car_antagonism
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.011 |
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".